Methods and materials for assessing and treating obesity

ABSTRACT

This document relates to methods and materials for assessing and/or treating obese mammals (e.g., obese humans). For example, methods and materials for using one or more interventions (e.g., one or more pharmacological interventions) to treat obesity and/or obesity-related comorbidities in a mammal (e.g., a human) identified as being likely to respond to a particular intervention (e.g., a pharmacological intervention) are provided.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Patent Application Ser. No.62/589,915, filed on Nov. 22, 2017. The disclosure of the priorapplication is considered part of (and is incorporated by reference in)the disclosure of this application.

STATEMENT REGARDING FEDERAL FUNDING

This invention was made with government support under DK067071 andDK084567 awarded by the National Institutes of Health. The governmenthas certain rights in the invention.

BACKGROUND 1. Technical Field

This document relates to methods and materials for assessing and/ortreating obesity in mammals (e.g., humans). For example, this documentprovides methods and materials for determining an obesity analytesignature of a mammal. For example, this document provides methods andmaterials for determining an obesity phenotype of a mammal. For example,this document provides methods and materials for using one or moreinterventions (e.g., one or more pharmacological interventions) to treatobesity and/or obesity-related comorbidities in a mammal (e.g., a human)identified as being likely to respond to a particular intervention(e.g., a pharmacological intervention).

2. Background Information

Obesity prevalence continues to increase worldwide (Ng et al., 2014Lancet 384:766-81) and, in the United States, 69% of adults areoverweight or obese (Flegal et al., 2012 JAMA 307:491-497). Estimatedcosts to the healthcare system are more than $550 billion annually.Increased severity of obesity correlates with a higher prevalence of theassociated co-morbidities. Likewise, obesity increases the risk ofpremature mortality (Hensrud et al., 2006 Mayo Clinic Proceedings 81(10Suppl):S5-10). Obesity affects almost every organ system in the body andincreases the risk of numerous diseases including type 2 diabetesmellitus, hypertension, dyslipidemia, cardiovascular disease, andcancer. It is estimated that a man in his twenties with a BMI over 45will have a 22% reduction (13 years) in life expectancy.

SUMMARY

Despite advances in understanding aspects of obesity pathophysiology,weight loss with current treatments including diet, exercise,medications, endoscopy; and surgery is highly variable (Acosta et al.,2014 Gut 63:687-95). For example, some obese patients specificallyrespond to particular medications, and can lose as much weight and withfewer side effects than bariatric surgery. There is a need to be able toidentify which intervention(s) an obese patient is likely to respond toin order to be able to select the right intervention for the rightpatient based on his/her pathophysiology.

This document provides methods and materials for assessing and/ortreating obesity in mammals (e.g., humans). In some cases, this documentprovides methods and materials for identifying an obese mammal as beingresponsive to a pharmacological intervention (e.g., by identifying themammal as having a pharmacotherapy responsive obesity analytesignature), and administering one or more interventions (e.g.,pharmacological interventions) to treat the mammal. For example, asample obtained from an obese mammal can be assessed to determine if theobese mammal is likely to be responsive to pharmacological interventionbased; at least in part; on an obesity phenotype, which is based, atleast in part, on an obesity analyte signature in the sample. Asdemonstrated herein, a distinct obesity analyte signature is present ineach of six main obesity phenotype groups: 1) low satiation, 2) lowsatiety (e.g., rapid return to hunger), 3) behavioral eating (identifiedby questionnaire), 4) large fasting gastric volume, 5) mixed, and 6) lowresting energy expenditure group; and each obesity phenotype is likelyto be responsive to one or more particular interventions (e.g.,pharmacological intervention, surgical intervention, weight loss device,diet intervention, behavior intervention, and/or microbiomeintervention).

Having the ability to identify which intervention(s) an obese patient islikely to respond to provides a unique and unrealized opportunity toprovide an individualized approach in selecting obesity treatments.

In general, one aspect of this document features a method for treatingobesity in a mammal. The method includes, or consists essentially of,identifying the mammal as having an intervention responsive obesityanalyte signature in a sample obtained from the mammal; andadministering an intervention to the mammal. The sample can be a bloodsample, a saliva sample, a urine sample, a breath sample, or a stoolsample. For example, the sample can be a breath sample. For example, themethod sample can be a stool sample. The mammal can be a human. In somecases, the obesity analyte signature can include 1-methylhistine,serotonin, glutamine, gamma-amino-n-butyric-acid, isocaproic,allo-isoleucine, hydroxyproline, beta-aminoisobutyric-acid, al anine,hexanoic, tyrosine, phenylalanine, ghrelin, and peptide tyrosinetyrosine (PYY). The intervention can be effective to reduce the totalbody weight of said mammal by at least 4%. The intervention can beeffective to reduce the total body weight of said mammal by from about 3kg to about 100 kg. The intervention can be effective to reduce thewaist circumference of said mammal by from about 1 inches to about 10inches. The identifying step also can include obtaining results from aHospital Anxiety and Depression Scale (HADS) questionnaire and/or aThree Factor Eating questionnaire (TFEQ). In some cases, the obesityanalyte signature can include a presence of serotonin, glutamine,isocaproic, allo-isoleucine, hydroxyproline, beta-aminoisobutyric-acid,alanine, hexanoic, tyrosine, and PYY, and an absence of (e.g., lacks thepresence of) 1-methylhistine, gamma-amino-n-butyric-acid, phenylalanine,ghrelin; the HADS questionnaire result does not indicate an anxietysubscale; and the mammal can be responsive to intervention withphentermine-topiramate pharmacotherapy and/or lorcaserinpharmacotherapy. In some cases, the obesity analyte signature caninclude a presence of 1-methylhistine, allo-isoleucine, hydroxyproline,beta-aminoisobutyric-acid, alanine, and phenylalanine, and an absence ofserotonin, glutamine, gamma-amino-n-butyric-acid, isocaproic, hexanoic,tyrosine, ghrelin, and PYY; the HADS questionnaire result not indicatean anxiety subscale; and the mammal can be responsive to interventionwith liraglutide pharmacotherapy. In some cases, the obesity analytesignature can include a presence of serotonin, and an absence of1-methylhistine, glutamine, gamma-amino-n-butyric-acid, isocaproic,allo-isoleucine, hydroxyproline, beta-aminoisobutyric-acid, alanine,hexanoic, tyrosine, phenylalanine, ghrelin, and PYY; the HADSquestionnaire result indicates an anxiety subscale; and the mammal canbe responsive to intervention with naltrexone-bupropion pharmacotherapy.In some cases, the obesity analyte signature can include a presence of1-methylhistine, glutamine, gamma-amino-n-butyric-acid, isocaproic,allo-isoleucine, beta-aminoisobutyric-acid, alanine, hexanoic, tyrosine,phenylalanine, PYY, and an absence of serotonin, hydroxyproline, andghrelin; the HADS questionnaire result indicates an anxiety subscale;and the mammal can be responsive to intervention withnaltrexone-bupropion pharmacotherapy. In some cases, the obesity analytesignature can include a presence of 1-methylhistine, serotonin,glutamine, gamma-amino-n-butyric-acid, isocaproic, allo-isoleucine,alanine, tyrosine, ghrelin, PYY, and an absence of hydroxyproline,beta-aminoisobutyric-acid, hexanoic, and phenylalanine; the HADSquestionnaire result indicates an anxiety subscale; and the mammal canbe responsive to intervention with phentermine pharmacotherapy. In somecases, the obesity analyte signature can include HTR2C, GNB3, FTO,iso-caproic acid, beta-aminoisobutyricacid, butyric, allo-isoleucine,tryptophan, and glutamine. The identifying step also can includeobtaining results from a HADS questionnaire. In some cases, the obesityanalyte signature can include the presence of a single nucleotidepolymorphism (SNP) in HTR2C, POMC, NPY, AGRP, MC4R, GNB3, SERT, and/orBDNF; the HADS questionnaire result does not indicate an anxietysubscale; and the mammal can be responsive to intervention withphentermine-topiramate pharmacotherapy and/or lorcaserinpharmacotherapy. The SNP can be rs1414334. In some cases, the obesityanalyte signature can include the presence of a SNP in PYY, GLP-1, MC4R,GPBAR1, TCF7L2, ADRA2A,PCSK, and/or TMEM18; the HADS questionnaireresult not indicate an anxiety subscale; and the mammal can beresponsive to intervention with liraglutide pharmacotherapy. The SNP canbe rs7903146. In some cases, the obesity analyte signature can includepresence of a SNP in SLC6A4/SERT, and/or DRD2; the HADS questionnaireresult indicates an anxiety subscale; and the mammal can be responsiveto intervention with naltrexone-bupropion pharmacotherapy. The SNP canbe rs4795541. In some cases, the obesity analyte signature can includethe presence of a SNP in TCF7L2, UCP3, and/or ADRA2A; the HADSquestionnaire result indicates an anxiety subscale; and the mammal canbe responsive to intervention with naltrexone-bupropion pharmacotherapy.The SNP can be rs1626521. In some cases, the obesity analyte signaturecan include the presence of a SNP in FTO, LEP, LEPR, UCP1, UCP2, UCP3,ADRA2, KLF14, NPC1, LYPLAL1, ADRB2, ADRB3, and/or BBS1; the HADSquestionnaire result indicates an anxiety subscale; and the mammal canbe responsive to intervention with phentermine pharmacotherapy. The SNPcan be rs2075577.

In another aspect, this document features a method for treating obesityin a mammal. The method includes, or consists essentially of,administering an intervention to a mammal that was identified as havingan intervention responsive obesity analyte signature. The mammal can bea human. The obesity analyte signature can include 1-methylhistine,serotonin, glutamine, gamma-amino-n-butyric-acid, isocaproic,allo-isoleucine, hydroxyproline, beta-aminoisobutyric-acid, alanine,hexanoic, tyrosine, phenylalanine, ghrelin, and peptide tyrosinetyrosine (PYY). The intervention can be effective to reduce the totalbody weight of said mammal by at least 4%. The intervention can beeffective to reduce the total body weight of said mammal by from about 3kg to about 100 kg. The intervention can be effective to reduce thewaist circumference of said mammal by from about 1 inches to about 10inches. The identifying step also can include obtaining results from aHospital Anxiety and Depression Scale (HADS) questionnaire. In somecases, the obesity analyte signature can include a presence ofserotonin, glutamine, isocaproic, allo-isoleucine, hydroxyproline,beta-aminoisobutyric-acid, alanine, hexanoic, tyrosine, and PYY, and anabsence of (e.g., lacks the presence of) 1-methylhistine,gamma-amino-n-butyric-acid, phenylalanine, ghrelin; the HADSquestionnaire result does not indicate an anxiety subscale; and themammal can be responsive to intervention with phentermine-topiramatepharmacotherapy and/or lorcaserin pharmacotherapy. In some cases, theobesity analyte signature can include a presence of 1-methylhistine,allo-isoleucine, hydroxyproline, beta-aminoisobutyric-acid, alanine, andphenylalanine, and an absence of serotonin, glutamine,gamma-amino-n-butyric-acid, isocaproic, hexanoic, tyrosine, ghrelin, andPYY; the HADS questionnaire result not indicate an anxiety subscale; andthe mammal can be responsive to intervention with liraglutidepharmacotherapy. In some cases, the obesity analyte signature caninclude a presence of serotonin, and an absence of 1-methylhistine,glutamine, gamma-amino-n-butyric-acid, isocaproic, allo-isoleucine,hydroxyproline, beta-aminoisobutyric-acid, alanine, hexanoic, tyrosine,phenylalanine, ghrelin, and PYY; the HADS questionnaire result indicatesan anxiety subscale; and the mammal can be responsive to interventionwith naltrexone-bupropion pharmacotherapy. In some cases, the obesityanalyte signature can include a presence of 1-methylhistine, glutamine,gamma-amino-n-butyric-acid, isocaproic, allo-isoleucine,beta-aminoisobutyric-acid, alanine, hexanoic, tyrosine, phenylalanine,PYY, and an absence of serotonin, hydroxyproline, and ghrelin; the HADSquestionnaire result indicates an anxiety subscale; and the mammal canbe responsive to intervention with naltrexone-bupropion pharmacotherapy.In some cases, the obesity analyte signature can include a presence of1-methylhistine, serotonin, glutamine, gamma-amino-n-butyric-acid,isocaproic, allo-isoleucine, alanine, tyrosine, ghrelin, PYY, and anabsence of hydroxyproline, beta-aminoisobutyric-acid, hexanoic, andphenylalanine; the HADS questionnaire result indicates an anxietysubscale; and the mammal can be responsive to intervention withphentermine pharmacotherapy.

In another aspect, this document features a method for identifying anobese mammal as being responsive to treatment with an intervention. Themethod includes, or consists essentially of, determining an obesityanalyte signature in a sample obtained from a mammal, where the obesityanalyte signature can include 1-methylhistine, serotonin, glutamine,gamma-amino-n-butyric-acid, isocaproic, allo-isoleucine, hydroxyproline,beta-aminoisobutyric-acid, alanine, hexanoic, tyrosine, phenylalanine,ghrelin, and PYY; and classifying the mammal as having an interventionresponsive obesity analyte signature based upon the presence and absenceof analytes in the obesity analyte signature. The mammal can be a human.The sample can be a blood sample, a saliva sample, a urine sample, abreath sample, or a stool sample. For example, the sample can be abreath sample. For example, the method sample can be a stool sample. Themethod also can include obtaining results from a HADS questionnaire. Insome cases, the obesity analyte signature can include a presence ofserotonin, glutamine, isocaproic, allo-isoleucine, hydroxyproline,beta-aminoisobutyric-acid, alanine, hexanoic, tyrosine, and PYY, and anabsence of (e.g., lacks the presence of) 1-methylhistine,gamma-amino-n-butyric-acid, phenylalanine, ghrelin; the HADSquestionnaire result does not indicate an anxiety subscale; and themammal can be responsive to intervention with phentermine-topiramatepharmacotherapy and/or lorcaserin pharmacotherapy. In some cases, theobesity analyte signature can include a presence of 1-methylhistine,allo-isoleucine, hydroxyproline, beta-aminoisobutyric-acid, alanine, andphenylalanine, and an absence of serotonin, glutamine,gamma-amino-n-butyric-acid, isocaproic, hexanoic, tyrosine, ghrelin, andPYY; the HADS questionnaire result not indicate an anxiety subscale; andthe mammal can be responsive to intervention with liraglutidepharmacotherapy. In some cases, the obesity analyte signature caninclude a presence of serotonin, and an absence of 1-methylhistine,glutamine, gamma-amino-n-butyric-acid, isocaproic, allo-isoleucine,hydroxyproline, beta-aminoisobutyric-acid, alanine, hexanoic, tyrosine,phenylalanine, ghrelin, and PYY; the HADS questionnaire result indicatesan anxiety subscale; and the mammal can be responsive to interventionwith naltrexone-bupropion pharmacotherapy. In some cases, the obesityanalyte signature can include a presence of 1-methylhistine, glutamine,gamma-amino-n-butyric-acid, isocaproic, allo-isoleucine,beta-aminoisobutyric-acid, alanine, hexanoic, tyrosine, phenylalanine,PYY, and an absence of serotonin, hydroxyproline, and ghrelin; the HADSquestionnaire result indicates an anxiety subscale; and the mammal canbe responsive to intervention with naltrexone-bupropion pharmacotherapy.In some cases, the obesity analyte signature can include a presence of1-methylhistine, serotonin, glutamine, gamma-amino-n-butyric-acid,isocaproic, allo-isoleucine, alanine, tyrosine, ghrelin, PYY, and anabsence of hydroxyproline, beta-aminoisobutyric-acid, hexanoic, andphenylalanine; the HADS questionnaire result indicates an anxietysubscale; and the mammal can be responsive to intervention withphentermine pharmacotherapy.

In another aspect, this document features a identifying an obese mammalas being responsive to treatment with an intervention. The methodincludes, or consists essentially of, determining an obesity analytesignature in a sample obtained from an obese mammal, where the obesityanalyte signature includes HTR2C, GNB3, FTO, iso-caproic acid,beta-aminoisobutyricacid, butyric, allo-isoleucine, tryptophan, andglutamine; obtaining results from a HADS questionnaire; and classifyingthe mammal as having a intervention responsive obesity analyte signaturebased upon the presence and absence of analytes in the obesity analytesignature. The mammal can be a human. The sample can be a blood sample,a saliva sample, a urine sample, a breath sample, or a stool sample. Insome cases, the sample can be a breath sample. In some cases, the samplecan be a stool sample. In some cases, the obesity analyte signature caninclude the presence of a SNP in HTR2C, POMC, NPY, AGRP, MC4R, GNB3,SERT, and/or BDNF; the HADS questionnaire result can not indicate ananxiety subscale; and the mammal can be classified as being responsiveto intervention with phentermine-topiramate pharmacotherapy and/orlorcaserin pharmacotherapy. The SNP can be rs1414334. In some cases, theobesity analyte signature can include the presence of a SNP in PYY,GLP-1, MC4R, GPBAR1, TCF7L2, ADRA2A,PCSK, and/or TMEM18; the HADSquestionnaire result can not indicate an anxiety subscale; and themammal can be classified as being responsive to intervention withliraglutide pharmacotherapy. The SNP can be rs7903146. In some cases,the obesity analyte signature can include the presence of a SNP inSLC6A4/SERT, and/or DRD2; the HADS questionnaire result can indicate ananxiety subscale; and the mammal can be classified as being responsiveto intervention with naltrexone-bupropion pharmacotherapy. The SNP canbe rs4795541. In some cases, the obesity analyte signature can includethe presence of a SNP in TCF7L2, UCP3, and/or ADRA2A; the HADSquestionnaire result can indicate an anxiety subscale; and the mammalcan be classified as being responsive to intervention withnaltrexone-bupropion pharmacotherapy. The SNP can be rs1626521. In somecases, the obesity analyte signature can include the presence of a SNPin FTO, LEP, LEPR, UCP1, UCP2, UCP3, ADRA2, KLF14, NPC1, LYPLAL1, ADRB2,ADRB3, and/or BBS1; the HADS questionnaire result can indicate ananxiety subscale; and the mammal can be classified as being responsiveto intervention with phentermine pharmacotherapy. The SNP can bers2075577.

Unless otherwise defined, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention pertains. Although methods and materialssimilar or equivalent to those described herein can be used to practicethe invention, suitable methods and materials are described below. Allpublications, patent applications, patents, and other referencesmentioned herein are incorporated by reference in their entirety. Incase of conflict, the present specification, including definitions, willcontrol. In addition, the materials, methods, and examples areillustrative only and not intended to be limiting.

The details of one or more embodiments of the invention are set forth inthe accompanying drawings and the description below. Other features,objects, and advantages of the invention will be apparent from thedescription and drawings, and from the claims.

DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1E shows classifications of obesity. A) 180 Caucasianparticipants with obesity (BMI>30 kg·m2) were sub classified into a)abnormal satiation (16%), abnormal satiety (16%), abnormalhedonic/behavior (19%), slow metabolism (32%) and mixed group (17%). Thesubgroups have unique characteristics as shown for food intake untilreaching fullness tested in a nutrient drink test (B), gastric emptyingrate, surrogate of satiety (C) and anxiety levels, surrogate of hedonic(D), and slow metabolism (E) based on the subgroups and gender(blue=females, red=males).

FIGS. 2A and 2B show biomarker discovery. A) Venn Diagrams of uniquemetabolites per obesity phenotype identified using positive-HILICuntargeted metabolomics. GP1—satiation; GP2—satiety (rapid return tohunger); Gp3—hedonic; and Gp4—energy expenditure. B) A score plot of aprincipal component analysis (PCA) of obesity phenotypes showing thatobesity phenotype groups can be separated based on metabolicdifferences.

FIG. 3 is a receiver operating characteristic (ROC) curve showing thesensitivity and specificity of determining an obesity phenotype based onmetabolic signature.

FIG. 4 is a ROC curve using Bayesian covariate predictors for lowsatiation, behavioral eating, and low resting energy expenditure.

FIG. 5 is a ROC curve showing the sensitivity and specificity ofdetermining an obesity phenotype based on metabolic signature.

FIG. 6 shows food intake meal paradigms measuring ‘maximal’ fullness(MTV), ‘usual’ fullness (VTF) in a nutrient drink test and ‘usual’fullness to mixed meal (solids) in an ad libitum buffet meal.

FIGS. 7A-7D shows abnormal satiety deeper phenotypes. A) Gastricemptying (GE) of solid T1/2 and T1/4 GE of liquids T1/5 for females andmales. B) Fasting and postprandial gastric volume for females and males.C) Postprandial PYY3-36 and GLP-1 at 90 minutes. D) correlation ofPostprandial PYY3-36 at 90 minutes and food intake by a nutrient drinktest.

FIGS. 8A-8B show hedonic group deeper phenotypes. A) Anxiety, depressionand self-esteem levels and B) fasting serum tryptophan levels inpatients with hedonic obesity compared to normal.

FIGS. 9A-9D show slow metabolism deeper phenotypes. A) Predicted restingenergy expenditure in patients with normal metabolism (other) comparedto slow metabolism by gender (data in percentage). B) Resting energyexpenditure in patients with normal metabolism (other) compared to slowmetabolism (data in kcal/day). C) Body composition in differentobesity-related phenotypes measured by DEXA. Top row is calculated BMI,med-row is total body fat and lower row is total lean mass. D) Levels ofmetabolites in patients with slow metabolism compared to normalmetabolism (other or rest). Metabolites describes are Alanine,isocaproic acid, phosphoetahnol amine, phenylalanine, tyrosine,alpha-amino-N-butyric acid, sarcasine, and 1-methylhistidine.

FIG. 10 is a bar graph showing body weight change in response totreatment with placebo or a combination of phentermine and topiramate(PhenTop) and kcal intake at prior ad-libitum meal (satiation test).

FIG. 11 is a bar graph showing body weight change in response totreatment with placebo or exenatide in patients with a particularobesity phenotype.

FIGS. 12A-12C are a flow charts showing exemplary treatmentinterventions for obesity groups identified based, at least in part, ona patient's obesity analyte signature.

FIG. 13 is a bar graph showing total body weight loss (TBWL) in responseto individualized intervention based on pre-selecting the specificindividual patient's obesity analyte signature.

FIG. 14 is a line graph showing TBWL in response to individualizedintervention over time.

DETAILED DESCRIPTION

This document provides methods and materials for assessing and/ortreating obesity in mammals (e.g., humans). In some cases, this documentprovides methods and materials for identifying an obese mammal as beingresponsive to a pharmacological intervention, and administering one ormore pharmacological interventions to treat the mammal. For example, asample obtained from an obese mammal can be assessed to determine if theobese mammal is likely to be responsive to intervention (e.g.,pharmacological intervention, surgical intervention, weight loss device,diet intervention, behavior intervention, and/or microbiomeintervention) based, at least in part, on an obesity phenotype, which isbased, at least in part, on an obesity analyte signature in the sample.An obesity analyte signature can include the presence, absence, or level(e.g., concentration) of two or more (e.g., three, four, five, six,seven, eight, nine, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, or more)obesity analytes (e.g., biomarkers associated with obesity). In somecases, an obesity analyte signature can include 14 obesity analytes. Forexample, a pharmacotherapy responsive obesity analyte signature can bebased, at least in part, on the presence, absence, or level of 14obesity analytes. In some cases, an obesity analyte signature caninclude 9 obesity analytes. For example, a pharmacotherapy responsiveobesity analyte signature can be based, at least in part, on thepresence, absence, or level of 9 obesity analytes. In some cases, themethods and materials described herein can be used to predict furtherweight loss response (e.g., during the course of an obesity treatment).In some cases, the methods and materials described herein can be used toprevent plateaus (e.g., during the course of an obesity treatment). Insome cases, the methods and materials described herein can be used toenhance weight loss maintenance (e.g., during the course of an obesitytreatment). In some cases, the methods and materials described hereincan be used to treat patients unable to lose and maintain weight withdiet and exercise alone.

As described herein, a distinct obesity analyte signature can be presentin each of six main obesity phenotypes: Group 1) low satiation, Group 2)low satiety (e.g., rapid return to hunger), Group 3) behavioral eating(e.g., as identified by questionnaire), Group 4) large fasting gastricvolume, Group 5) mixed, and Group 6) low resting energy expendituregroup. Also described herein, the obesity analyte signature in sampleobtained from an obese mammal (and thus the obesity phenotype) can beused to predict intervention responsiveness. In some cases, obesityphenotype groups can be simplified as: 1) high energy intake, 2)behavioral/emotional eating, and 3) low energy expenditure; or can besimplified as 1) low satiation (fullness), 2) low satiety (return tohunger), 3) behavioral/emotional eating, 4) low energy expenditure, 5)mixed, and 6) other.

When treating obesity in a mammal (e.g., a human) as described herein,the mammal can also have one or more obesity-related (e.g.,weight-related) co-morbidities. Examples of weight-relatedco-morbidities include, without limitation, hypertension, type 2diabetes, dyslipidemia, obstructive sleep apnea, gastroesophageal refluxdisease, weight baring joint arthritis, cancer, non-alcoholic fattyliver disease, nonalcoholic steatohepatitis, depression, anxiety, andatherosclerosis (coronary artery disease and/or cerebrovasculardisease). In some cases, the methods and materials described herein canbe used to treat one or more obesity-related co-morbidities.

When treating obesity in a mammal (e.g., a human) as described herein,the treatment can be effective to reduce the weight, reduce the waistcircumference, slow or prevent weight gain of the mammal, improve thehemoglobin A1c, and/or improve the fasting glucose. For example,treatment described herein can be effective to reduce the weight (e.g.,the total body weight) of an obese mammal by at least 3% (e.g., at least5%, at least 8%, at least 10%, at least 12%, at least 15%, at least 18%,at least 20%, at least 22%, at least 25%, at least 28%, at least 30%, atleast 33%, at least 36%, at least 39%, or at least 40%). For example,treatment described herein can be effective to reduce the weight (e.g.,the total body weight) of an obese mammal by from about 3% to about 40%(e.g., from about 3% to about 35%, from about 3% to about 30%, fromabout 3% to about 25%, from about 3% to about 20%, from about 3% toabout 15%, from about 3% to about 10%, from about 3% to about 5%, fromabout 5% to about 40%, from about 10% to about 40%, from about 15% toabout 40%, from about 20% to about 40%, from about 25% to about 40%,from about 35% to about 40%, from about 5% to about 35%, from about 10%to about 30%, from about 15% to about 25%, or from about 18% to about22%). For example, treatment described herein can be effective to reducethe weight (e.g., the total body weight) of an obese mammal by fromabout 3 kg to about 100 kg (e.g., about 5 kg to about 100 kg, about 8 kgto about 100 kg, about 10 kg to about 100 kg, about 15 kg to about 100kg, about 20 kg to about 100 kg, about 30 kg to about 100 kg, about 40kg to about 100 kg, about 50 kg to about 100 kg, about 60 kg to about100 kg, about 70 kg to about 100 kg, about 80 kg to about 100 kg, about90 kg to about 100 kg, about 3 kg to about 90 kg, about 3 kg to about 80kg, about 3 kg to about 70 kg, about 3 kg to about 60 kg, about 3 kg toabout 50 kg, about 3 kg to about 40 kg, about 3 kg to about 30 kg, about3 kg to about 20 kg, about 3 kg to about 10 kg, about 5 kg to about 90kg, about 10 kg to about 75 kg, about 15 kg to about 50 kg, about 20 kgto about 40 kg, or about 25 kg to about 30 kg). For example, treatmentdescribed herein can be effective to reduce the waist circumference ofan obese mammal by from about 1 inches to about 10 inches (e.g., about 1inches to about 9 inches, about 1 inches to about 8 inches, about 1inches to about 7 inches, about 1 inches to about 6 inches, about 1inches to about 5 inches, about 1 inches to about 4 inches, about 1inches to about 3 inches, about 1 inches to about 2 inches, about 2inches to about 10 inches, about 3 inches to about 10 inches, about 4inches to about 10 inches, about 5 inches to about 10 inches, about 6inches to about 10 inches, about 7 inches to about 10 inches, about 8inches to about 10 inches, about 9 inches to about 10 inches, about 2inches to about 9 inches, about 3 inches to about 8 inches, about 4inches to about 7 inches, or about 5 inches to about 7 inches). In somecases, the methods and materials described herein can be used to improve(e.g., increase or decrease) the hemoglobin A1c of an obese mammal(e.g., an obese mammal having type 2 diabetes mellitus) to from about0.4% to about 3% (e.g., from about 0.5% to about 3%, from about 1% toabout 3%, from about 1.5% to about 3%, from about 2% to about 3%, fromabout 2.5% to about 3%, from about 0.4% to about 2.5%, from about 0.4%to about 2%, from about 0.4% to about 1.5%, from about 0.4% to about 1%,from about 0.5% to about 2.5%, or from about 1% to about 2%) hemoglobinA1c. In some cases, the methods and materials described herein can beused to improve (e.g., increase or decrease) the fasting glucose of anobese mammal (e.g., an obese mammal having type 2 diabetes mellitus) tofrom about 10 mg/dl to about 200 mg/dl (e.g., from about 15 mg/dl toabout 200 mg/dl, from about 25 mg/dl to about 200 mg/dl, from about 50mg/di to about 200 mg/dl, from about 75 mg/dl to about 200 mg/dl, fromabout 100 mg/dl to about 200 mg/dl, from about 125 mg/dl to about 200mg/dl, from about 150 mg/dl to about 200 mg/dl, from about 175 mg/di toabout 200 mg/dl, from about 190 mg/dl to about 200 mg/dl, from about 10mg/dl to about 175 mg/dl, from about 10 mg/dl to about 150 mg/dl, fromabout 10 mg/dl to about 125 mg/dl, from about 10 mg/dl to about 100mg/dl, from about 10 mg/dl to about 75 mg/dl, from about 10 mg/dl toabout 50 mg/dl, from about 10 mg/dl to about 25 mg/dl, or from about 10mg/dl to about 20 mg/dl) glucose.

Any type of mammal can be assessed and/or treated as described herein.Examples of mammals that can be assessed and/or treated as describedherein include, without limitation, primates (e.g., humans and monkeys),dogs, cats, horses, cows, pigs, sheep, rabbits, mice, and rats. In somecases, the mammal can a human. In some cases, a mammal can be an obesemammal. For example, obese humans can be assessed for intervention(e.g., a pharmacological intervention) responsiveness, and treated withone or more interventions as described herein. In cases where mammal isa human, the human can be of any race. For example, a human can beCaucasian or Asian.

Any appropriate method can be used to identify a mammal as beingoverweight (e.g., as being obese). In some cases, calculating body massindex (BMI), measuring waist and/or hip circumference, health history(e.g., weight history, weight-loss efforts, exercise habits, eatingpatterns, other medical conditions, medications, stress levels, and/orfamily health history), physical examination (e.g., measuring yourheight, checking vital signs such as heart rate blood pressure,listening to your heart and lungs, and examining your abdomen),percentage of body fat and distribution, percentage of visceral andorgans fat, metabolic syndrome, and/or obesity related comorbidities canbe used to identify mammals (e.g., humans) as being obese. For example,a BMI of greater than about 30 kg/m² can be used to identify mammals(e.g., Caucasian humans) as being obese. For example, a BMI of greaterthan about 27 kg/m² with a co-morbidity can be used to identify mammals(e.g., Asian humans) as being obese.

Once identified as being obese, a mammal can be assessed to determinewhether or not it is likely to respond to one or more interventions(e.g., pharmacological intervention, surgical intervention, weight lossdevice, diet intervention, behavior intervention, and/or microbiomeintervention). For example, a sample obtained from the mammal can beassessed for pharmacological intervention responsiveness. As describedherein, a panel of obesity analytes in a sample obtained from an obesemammal can be used to determine an obesity analyte signature of themammal, and can be used in to determine an obesity phenotype of themammal.

Any appropriate sample from a mammal (e.g., a human) having obesity canbe assessed as described herein. In some cases, a sample can be abiological sample. In some cases, a sample can contain obesity analytes(e.g., DNA, RNA, proteins, peptides, metabolites, hormones, and/orexogenous compounds (e.g. medications)). Examples of samples that can beassessed as described herein include, without limitation, fluid samples(e.g., blood, serum, plasma, urine, saliva, sweat, or tears), breathsamples, cellular samples (e.g., buccal samples), tissue samples (e.g.,adipose samples), stool samples, gastro samples, and intestinal mucosasamples. In some cases, a sample (e.g., a blood sample) can be collectedwhile the mammal is fasting (e.g., a fasting sample such as a fastingblood sample). In some cases, a sample can be processed (e.g., toextract and/or isolate obesity analytes). For example, a serum samplecan be obtained from an obese mammal and can be assessed to determine ifthe obese mammal is likely to be responsive to one or more interventions(e.g., pharmacological intervention, surgical intervention, weight lossdevice, diet intervention, behavior intervention, and/or microbiomeintervention) based, at least in part, on an obesity phenotype, which isbased, at least in part, on an obesity analyte signature in the sample.For example, a urine sample can be obtained from an obese mammal and canbe assessed to determine if the obese mammal is likely to be responsiveto pharmacological intervention based, at least in part, on an obesityphenotype, which is based, at least in part, on an obesity analytesignature in the sample.

An obesity analyte signature can include any appropriate analyte.Examples of analytes that can be included in an obesity analytesignature described herein include, without limitation, DNA, RNA,proteins, peptides, metabolites, hormones, and exogenous compounds (e.g.medications). An obesity analyte signature can be evaluated using anyappropriate methods. For example, metabolomics, genomics, microbiome,proteomic, peptidomics, and behavioral questionnaires can be used toevaluate and/or identify an obesity analyte signature described herein.

Any appropriate method can be used to identify an obesity phenotype asdescribed herein. In some cases, the obesity phenotype can be identifiedas described in the Examples. For example, the obesity phenotype can beidentified by determining the obesity analyte signature in a sample(e.g., in a sample obtained from an obese mammal). In some cases, theobesity analyte signature can be obtained by detecting the presence,absence, or level of one or more metabolites, detecting the presence, rabsence, or level one or more peptides (e.g., gastrointestinalpeptides), and/or detecting the presence, absence, or level of one ormore single nucleotide polymorphisms (SNPs).

A metabolite can be any metabolite that is associated with obesity. Insome cases, a metabolite can be an amino-compound. In some cases, ametabolite can be a neurotransmitter. In some cases, a metabolite can bea fatty acid (e.g., a short chain fatty acid). In some cases, ametabolite can be an amino compound. In some cases, a metabolite can bea bile acid. In some cases, a metabolite can be a compound shown inTable 2. Examples of metabolites that can be used to determine theobesity analyte signature in a sample (e.g., in a sample obtained froman obese mammal) include, without limitation, 1-methylhistine,serotonin, glutamine, gamma-amino-n-butyric-acid, isocaproic,allo-isoleucine, hydroxy-proline, beta-aminoisobutyric-acid, alanine,hexanoic, tyrosine, phenylalanine γ-aminobutyric acid, acetic,histidine, LCA, ghrelin, ADRA2A, cholesterol, glucose, acetylcholine,propionic, CDCA, PYY, ADRA2C, insulin, adenosine, isobutyric,1-methylhistidine, DCA, CCK, GNB3, glucagon, aspartate, butyric,3-methylhistidine, UDCA, GLP-1, FTO, leptin, dopamine, valeric,asparagine, HDCA, GLP-2, MC4R, adiponectin, D-serine, isovaleric,phosphoethanolamine, CA, glucagon, TCF7L2, glutamate, hexanoic,arginine, GLCA, oxyntomodulin, 5-HTTLPR, glycine, octanoic, carnosine,GCDCA, neurotensin, HTR2C, myristic, taurine, GDCA, FGF, UCP2,norepinephrine, palmitic, anserine, GUDCA, GIP, UCP3, serotonin,palmitoleic, serine, GHDCA, OXM, GPBAR1, taurine, palmitelaidic,glutamine, GCA, FGF19, NR1H4, stearic, ethanolamine, TLCA, FGF21, FGFR4,oleic, glycine, TCDCA, LDL, elaidic, aspartic acid, TDCA, insulin,GLP-1, linoleic, sarcosine, TUDCA, glucagon, CCK, a-linolenic, proline,THDCA, amylin, arachidonic, alpha-aminoadipic-acid, TCA, pancreaticpolypeptide, eicosapentaenoic, DHCA, neurotensin, docosahexaenoic,alpha-amino-N-butyric-acid, THCA, ornithine, GLP-1 receptor,triglycerides, cystathionine 1, GOAT, cystine, DPP4, lysine, methionine,valine, isoleucine, leucine, homocystine, tryptophan, citrulline,glutamic acid, beta-alanine, threonine, hydroxylysine 1, acetone, andacetoacetic acid. In some cases, an obesity analyte signature caninclude 1-methylhistine, serotonin, glutamine,gamma-amino-n-butyric-acid, isocaproic, allo-isoleucine, hydroxyproline,beta-aminoisobutyric-acid, alanine, hexanoic, tyrosine, andphenylalanine.

A gastrointestinal peptide can be any gastrointestinal peptide that isassociated with obesity. In some cases, a gastrointestinal peptide canbe a peptide hormone. In some cases, a gastrointestinal peptide can bereleased from gastrointestinal cells in response to feeding. In somecases, a gastrointestinal peptide can be a peptide shown in Table 2.Examples of gastrointestinal peptides that can be used to determine theobesity analyte signature in a sample (e.g., in a sample obtained froman obese mammal) include, without limitation, ghrelin, peptide tyrosinetyrosine (PYY), cholecystokinin (CCK), glucagon-like peptide-1 (GLP-1),GLP-2, glucagon, oxyntomodulin, neurotensin, fibroblast growth factor(FGF), GIP, OXM, FGF19, FGF19, and pancreatic polypeptide.

A SNP can be any SNP that is associate with obesity. A SNP can be in acoding sequence (e.g., in a gene) or a non-coding sequence. For example,in cases where a SNP is in a coding sequence, the coding sequence can beany appropriate coding sequence. In some cases, a coding sequence thatcan include a SNP associated with obesity can be a gene shown in Table2. Examples of coding sequences that a SNP associated with obesity canbe in or near include, without limitation, ADRA2A, ADRA2C, GNB3, FTO,MC4R, TCF7L2, 5-HTTLPR, HTR2C, UCP2, UCP3, GPBAR1, NR1H4, FGFR4, PYY,GLP-1, CCK, leptin, adiponectin, neurotensin, ghrelin, GLP-1 receptor,GOAT, DPP4, POMC, NPY, AGRP, SERT, BDNF, SLC6A4, DRD2, LEP, LEPR, UCP1,KLF14, NPC1, LYPLAL1, ADRB2, ADRB3, BBS1, ACSL6, ADARB2, ADCY8, ADH1B,AJAP1, ATP2C2, ATP6V0D2, C21orf7, CAMKMT, CAP2, CASC4, CD48, CDC42SE2,CDYL, CES5AP1, CLMN, CNPY4, COL19A1, COL27A1, COL4A3, CORO1C, CPZ, CTIF,DAAM2, DCHS2, DOCK8, EGFLAM, FAM125B, FAM71E2, FRMD3, GALNTL4, GLT1D1,HHAT, KRT23, LHPP, L1NC00578, LINC00620, LIPC, LOC100128714,LOC100287160, LOC 100289473, LOC100293612|LINC00620, LOC100506869,LOC100507053, LOC100507053|ADH1A, LOC100507053|ADH, LOC100507443,LOC1009965711|CYYR1, LOC152225, LOC255130, LPAR1, LUZP2, MCM7, MICAL3,MMS19, MYBPC1, NR2F2-AS1, NSMCE2, NTN1, O3FAR1, OAZ2, OSBP2, P4HA2,PADI1, PARD3B, PARK2, PCDH15, PIEZO2, PKIB, PRH1-PRR4, PTPRD,RALGPS1|ANGPTL2, RPS24P10, RTN4RL1, RYR2, SCN2A, SEMA3C, SEMA5A, SFMBT2,SGCG, SLC22A15, SLC2A2, SLCO1B1, SMOC2,SNCAIP, SNX18, SRRM4, SUSD1,TBC1D16, TCERG1L, TENM3, TJP3, TLL1, TMEM9B, TPM1, VTI1A, VWF, WWOX,WWTR1, ZFYVE28, ZNF3, ZNF609, and ZSCAN21. In some cases, a SNP can be aSNP shown in Table 3. Examples of SNPS that can be used to determine theobesity analyte signature in a sample (e.g., in a sample obtained froman obese mammal) include, without limitation, rs657452, rs11583200,rs2820292, rs11126666, rs11688816, rs1528435, rs7599312, rs6804842,rs2365389, rs3849570, rs16851483, rs17001654, rs11727676, rs2033529,rs9400239, rs13191362, rs1167827, rs2245368, rs2033732, rs4740619,rs6477694, rs1928295, rs10733682, rs7899106, rs17094222, rs11191560,rs7903146, rs2176598, rs12286929, rs11057405, rs10132280, rs12885454,rs3736485, rs758747, rs2650492, rs9925964, rs1000940, rs1808579,rs7243357, rs17724992, rs977747, rs1460676, rs17203016, rs13201877,rs1441264, rs7164727, rs2080454, rs9914578, rs2836754, rs492400,rs16907751, rs9374842, rs9641123, rs9540493, rs4787491, rs6465468,rs7239883, rs3101336, rs12566985, rs12401738, rs11165643, rs17024393,rs543874, rs13021737, rs10182181, rs1016287, rs2121279, rs13078960,rs1516725, rs10938397, rs13107325, rs2112347, rs205262, rs2207139,rs17405819, rs10968576, rs4256980, rs11030104, rs3817334, rs7138803,rs12016871, rs12429545, rs11847697, rs7141420, rs16951275, rs12446632,rs3888190, rs1558902, rs12940622, rs6567160, rs29941, rs2075650,rs2287019, rs3810291, rs7715256, rs2176040, rs6091540, rs1800544,Ins-Del-322, rs5443, rs1129649, rs1047776, rs9939609, rs17782313,rs7903146, rs4795541, rs3813929, rs518147, rs1414334, rs659366, -3474,rs2075577, rs15763, rs1626521, rs11554825, rs4764980, rs434434,rs351855, and rs2234888.

An obesity analyte signature described herein can include anyappropriate combination of analytes. For example, when an obesityanalyte signature includes 14 analytes, the analytes can include1-methylhistine, serotonin, glutamine, gamma-amino-n-butyric-acid,isocaproic, allo-isoleucine, hydroxyproline, beta-aminoisobutyric-acid,alanine, hexanoic, tyrosine, phenylalanine, ghrelin, and PYY. Forexample, when an obesity analyte signature includes 9 analytes, theanalytes can include HTR2C, GNB3, FTO, isocaproic,beta-aminoisobutyric-acid, butyric, allo-isoleucine, tryptophan, andglutamine.

Any appropriate method can be used to detect the presence, absence, orlevel of an obesity analyte within a sample. For example, massspectrometry (e.g., triple-stage quadrupole mass spectrometry coupledwith ultra-performance liquid chromatography (UPLC)), radioimmunoassays, and enzyme-linked immunosorbent assays can be used to determinethe presence, absence, or level of one or more analyte in a sample.

In some cases, identifying the obesity phenotype can include obtainingresults from all or part of one or more questionnaires. A questionnairecan be associated with obesity. In some cases, a questionnaire can beanswered the time of the assessment. In some cases, a questionnaire canbe answered prior to the time of assessment. For example, when aquestionnaire is answered prior to the time of the assessment, thequestionnaire results can be obtained by reviewing a patient history(e.g., a medical chart). A questionnaire can be a behavioralquestionnaire (e.g., psychological welfare questionnaires, alcohol usequestionnaires, eating behavior questionnaires, body imagequestionnaires, physical activity level questionnaire, and weightmanagement questionnaires. Examples of questionnaires that can be usedto determine the obesity phenotype of a mammal (e.g., an obese mammal)include, without limitation, The Hospital Anxiety and Depression Scale(HADS) questionnaire, The Hospital Anxiety and Depression Inventoryquestionnaire, The Questionnaire on Eating and Weight Patterns, TheWeight Efficacy Life-Style (WEL) Questionnaire, The MultidimensionalBody-Self Relations Questionnaire, The Questionnaire on Eating andWeight Patterns-Revised, The Weight Efficacy Life-Style, PhysicalActivity Level-item Physical Activity Stages of Change Questionnaire,The Exercise Regulations Questionnaire (BREQ-3), Barriers to BeingActive Quiz, and The Three Factor Eating Questionnaire (TFEQ). Forexample, a questionnaire can be a HADS questionnaire. For example, aquestionnaire can be a TFEQ.

In some cases, an obesity analyte signature can include the presence ofserotonin, glutamine, isocaproic, allo-isoleucine, hydroxyproline,beta-aminoisobutyric-acid, alanine, hexanoic, tyrosine, and PYY. Forexample, an obesity phenotype Group 1 can have an obesity analytesignature that includes the presence of serotonin, glutamine,isocaproic, allo-isoleucine, hydroxyproline, beta-aminoisobutyric-acid,alanine, hexanoic, tyrosine, and PYY. For example, an obesity phenotypeGroup 1 can have an obesity analyte signature that has an absence of(e.g., lacks the presence of) 1-methylhistine,gamma-amino-n-butyric-acid, phenylalanine, ghrelin, and includes a HADSquestionnaire result that does not indicate an anxiety subscale (HADS-A;e.g., includes a HADS-A questionnaire result).

In some cases, an obesity analyte signature can include the presence of1-methylhistine, allo-isoleucine, hydroxyproline,beta-aminoisobutyric-acid, alanine, and phenylalanine. For example, anobesity phenotype Group 2 can have an obesity analyte signature thatincludes the presence of -methylhistine, allo-isoleucine,hydroxyproline, beta-aminoisobutyric-acid, alanine, and phenylalanine.For example, an obesity phenotype Group 2 can have an obesity analytesignature that has an absence of (e.g., lacks the presence of)serotonin, glutamine, gamma-amino-n-butyric-acid, isocaproic, hexanoic,tyrosine, ghrelin, PYY, and does not include a HADS questionnaire resultthat indicates an anxiety subscale (e.g., does not include a HADS-Aquestionnaire result)

In some cases, an obesity analyte signature can include the presence ofserotonin, and can include a HADS-A questionnaire. For example, anobesity phenotype Group 3 can have an obesity analyte signature thatincludes serotonin and includes a HADS-A questionnaire result. Forexample, an obesity phenotype Group 3 can have an obesity analytesignature that has an absence of (e.g., lacks the presence of)1-methylhistine, glutamine, gamma-amino-n-butyric-acid, isocaproic,allo-isoleucine, hydroxyproline, beta-aminoisobutyric-acid, alanine,hexanoic, tyrosine, phenylalanine, ghrelin, and PYY.

In some cases, an obesity analyte signature can include the presence of1-methylhistine, glutamine, gamma-amino-n-butyric-acid, isocaproic,allo-isoleucine, beta-aminoisobutyric-acid, alanine, hexanoic, tyrosine,phenylalanine, PYY, and includes a HADS-A questionnaire result. Forexample, an obesity phenotype Group 4 can have an obesity analytesignature that includes 1-methylhistine, glutamine,gamma-amino-n-butyric-acid, isocaproic, allo-isoleucine,beta-aminoisobutyric-acid, alanine, hexanoic, tyrosine, phenylalanine,PYY, and includes a HADS-A questionnaire result. For example, an obesityphenotype Group 4 can have an obesity analyte signature that has anabsence of (e.g., lacks the presence of) serotonin, hydroxyproline, andghrelin.

In some cases, an obesity analyte signature can include the presence ofserotonin, beta-aminoisobutyric-acid, alanine, hexanoic, phenylalanine,and includes a HADS-A questionnaire. For example, an obesity phenotypeGroup 5 can have an obesity analyte signature that includes the presenceof serotonin, beta-aminoisobutyric-acid, alanine, hexanoic,phenylalanine, and includes a HADS-A questionnaire result. For example,an obesity phenotype Group 5 can have an obesity analyte signature thathas an absence of (e.g., lacks the presence of) 1-methylhistine,glutamine, gamma-amino-n-butyric-acid, isocaproic, allo-isoleucine, andhydroxyproline.

In some cases, an obesity analyte signature can include the presence of1-methylhistine, serotonin, glutamine, gamma-amino-n-butyric-acid,isocaproic, allo-isoleucine, alanine, tyrosine, ghrelin, PYY, andincludes a HADS-A questionnaire result. For example, an obesityphenotype Group 6 can have an obesity analyte signature that includesthe presence of 1-methylhistine, serotonin, glutamine,gamma-amino-n-butyric-acid, isocaproic, allo-isoleucine, alanine,tyrosine, ghrelin, PYY, and includes a HADS-A questionnaire result. Forexample, an obesity phenotype Group 6 can have an obesity analytesignature that has an absence of (e.g., lacks the presence of)hydroxyproline, beta-aminoisobutyric-acid, hexanoic, and phenylalanine.

In some cases, identifying the obesity phenotype also can includeidentifying one or more additional variables and/or one or moreadditional assessments. For example, identifying the obesity phenotypealso can include assessing the microbiome of a mammal (e.g., an obesemammal). For example, identifying the obesity phenotype also can includeassessing leptin levels. For example, identifying the obesity phenotypealso can include assessing the metabolome of a mammal (e.g., an obesemammal). For example, identifying the obesity phenotype also can includeassessing the genome of a mammal (e.g., an obese mammal). For example,identifying the obesity phenotype also can include assessing theproteome of a mammal (e.g., an obese mammal). For example, identifyingthe obesity phenotype also can include assessing the peptidome of amammal (e.g., an obese mammal).

Once the obesity phenotype of the mammal has been identified, the mammalcan be assessed to determine intervention (e.g., pharmacologicalintervention, surgical intervention, weight loss device, dietintervention, behavior intervention, and/or microbiome intervention)responsiveness, and a treatment option for the mammal can be selected.In some cases, the obesity phenotype of a mammal can be used to select atreatment options as shown in FIG. 12 , and as set forth in Table 1.

TABLE 1 Treatment options. Obesity Phenotype Group PharmacotherapyExemplary Intervention Pharmacotherapy Intervention: FDA approvedmedications 1: Iow satiation appetite suppressant in combinationphentermine-topiramate with an anticonvusIant appetite suppressantIorcaserin, desvenIafaxine 2: Iow satiety GLP-1 anaIog, GLP-1 receptorIiragIutide, exenatide, metformin, agonist, amyIin anaIogs pramIitide 3:behavioraI eating antidepressant in combination withnaItrexone-bupropion an opioid antagonist 4: Iargc fasting gastricantidcprcssant in combination with naItrcxonc-bupropion voIume an opioidantagonist 5: mixed combination based on the combination of phenotypes6: Iow resting energy appetite suppressant in combination phentermine +increased physicaI cxpcnditurc with physicaI activity activityPharmacotherapy Intervention: medications not-FDA approved 1: Iowsatiation meIanocortin receptor MK-0493 RM-493 appetite suppressants CCKanaIogs 2: Iow satiety GLP-1 anaIog - GLP-1 receptor semagIutide,agonists - GLP-1/gIucagon veInerperit (s-2367) coagonists obinepitidePYY anaIogs - Y receptors Conjugated biIe acids agonists/antagonistsOxyntomoduIin anaIogs GhreIin antagonists TGR5 agonists FGF-19/21anaIogs FXR agonists GRP-119 GRP-120 Combinations of these meds 3:bchavioraI cating antidcprcssant bupropion + zonisamidc opioidantagonist Tesofensine anti-anxiety Buspirone cannabionids antagonistsrimonabant 4: Iargc fasting gastric ghrcIin antagonist voIume 5: mixedcombination based on the combination of phenotypes 6: Iow resting energyIeptin moduIators metroIeptin expenditure MetAP2 inhibitors ZGN-1061,beIoranib B3 agonists mirabegron Weight Loss Devices Obesity PhenotypeGroup Surgical procedure and devices Exemplary Intervention 1: Iowsatiation vagaI stimuIant V-bIoc mouth occupying devices Retrogradegastric pacing intra-gastric space occupying Smartbyte ™ devices gastricbaIIoon sIeeve gastropIasty 2: Iow satiety duodenaI bypass or mucosaIEndobarrier resurfacing (exampIe: abIation) gastric baIIoonintra-gastric space occupying transpyIoric shuttIe devices maIabsorptiveprocedures 3: behavioraI eating gastric emptying devices Aspire assist4: Iargc fasting gastric gastric cmptying dcviccs Aspirc assist voIumeintra-gastric space occupying gastric baIIoon devices sIeevegastropIasty 5: mixed combination based on the combination of phcnotypcs6: Iow resting energy phentermine + increased physicaI expenditureactivity 1: Iow satiation Gastric occupying space TransoraI endoscopicrestrictive Brain stimuIant impIant system deep transcraniaI magneticstimuIation 2: Iow satiety DuodenaI bypass or mucosaI FractyI - duodenaIabIation rcsurfacing (cxampIc: abIation) Intragastric baIIoons -adjustabIe Intra-gastric space occupying Magnet therapy (Incision-Iessdevices Anastomosis System) MaIabsorptive procedures 3: behavioraIeating 4: Iarge fasting gastric Intra-gastric space occupyingIntragastric baIIoons - adjustabIe voIume devices POSE GastricpIications 5: mixed combination based on the combination of phenotypes6: Iow resting energy MuscIe stimuIants PuIse muscIe stimuIatorexpenditure Energy trackers CoId vests CoId inducers (stimuIates BAT)Diet Intervention Obesity Phenotype Group Diet Exemplary Intervention 1:Iow satiation SIow eating Legnmes, fruits, beans, whoIe grainsvoIumetric diet Atkins diet high fat - high protein - Iow carb Keto diet2: Iow saticty High protcin - Iow carb - avcragc PaIco-dict fatMediterranean diet 3: behavioraI eating ScheduIe 2-3 meaIs daiIy. Nosnacks Crash diet 4: Iarge fasting gastric High soIubIe fiber FibersuppIements, voIume 5: mixed 6: Iow rcsting cncrgy Low fat - Avcragcprotcin, avcragc 13-day MctaboIism dict expenditure carbs SurgicaIIntervention Obesity Phenotype Group Surgical procedure ExemplaryIntervention 1: Iow satiation Restrictive procedures SIeeve RYGBLap-band 2: Iow satiety MaIabsorptive procedures RYGB - SIeeve pIusduodenaI switch 3: behavioraI eating 4: Iarge fasting gastricRestrictive procedures SIeeve voIume RYGB 5: mixed 6: Iow resting energyMaIabsorptive procedures RYGB - duodenaI switch expenditure MicrobiomeIntervention Obesity Phenotype Group Microbiome status ExemplaryIntervention 1: Iow satiation Microbiota inflammatory inducing Reducemicrobiome LPS induction 2: Iow satiety Low microbiome richness Increaserichness of microbiota (probiotic mix) to increase SCFA in GI Iumen 3:behavioraI eating Serotonin producing bacteria Reduced serotoninproducing bacteria: restore Bacteroides spp 4: Iarge fasting gastric Lowmicrobiome richness Increase primary BA microbiota voIume 5: mixed 6:Iow resting energy Low fatty acids producing bacteria Increase fattyacid metaboIism expenditure producing bacteria

Individualized pharmacological interventions for the treatment ofobesity (e.g., based on the obesity phenotypes as described herein) caninclude any one or more (e.g., 1, 2, 3, 4, 5, 6, or more)pharmacotherapies (e.g., individualized pharmacotherapies). Apharmacotherapy can include any appropriate pharmacotherapy. In somecases, a pharmacotherapy can be an obesity pharmacotherapy. In somecases, a pharmacotherapy can be an appetite suppressant. In some cases,a pharmacotherapy can be an anticonvulsant. In some cases, apharmacotherapy can be a GLP-1 agonist. In some cases, a pharmacotherapycan be an antidepressant. In some cases, a pharmacotherapy can be anopioid antagonist. In some cases, a pharmacotherapy can be a controlledrelease pharmacotherapy. For example, a controlled releasepharmacotherapy can be an extended release (ER) and/or a slow release(SR) pharmacotherapy. In some cases, a pharmacotherapy can be a lipaseinhibitor. In some cases, a pharmacotherapy can be a DPP4 inhibitor. Insome cases, a pharmacotherapy can be a SGLT2 inhibitor. In some cases, apharmacotherapy can be a dietary supplement. Examples ofpharmacotherapies that can be used in an individualized pharmacologicalintervention as described herein include, without limitation, orlistat,phentermine, topiramate, lorcaserin, naltrexone, bupropion, liraglutide,exenatide, metformin, pramlitide, Januvia, canagliflozin,dexamphetamines, prebiotics, probiotics, Ginkgo biloba, and combinationsthereof. For example, combination pharmacological interventions for thetreatment of obesity (e.g., based on the obesity phenotypes as describedherein) can include phentermine-topiramate ER, naltrexone-bupropion SR,phentermine-lorcaserin, lorcaserin-liraglutide, and lorcarserin-januvia.A pharmacotherapy can be administered using any appropriate methods. Insome cases, pharmacotherapy can be administered by continuous pump, slowrelease implant, intra-nasal administered, intra-oral administered,and/or topical administered. In some cases, a pharmacotherapy can beadministered as described elsewhere (see, e.g., Sjostrom et al., 1998Lancet 352:167-72; Hollander et al., 1998 Diabetes Care 21:1288-94;Davidson et al., 1999 JAMA 281:235-42; Gadde et al., 2011 Lancet377:1341-52; Smith et al., 2010 New Engl. J. Med. 363:245-256; Apovianet al., 2013 Obesity 21:935-43; Pi-Sunyer et al., 2015 New Engl. J. Med.373:11-22; and Acosta et al., 2015 Clin Gastroenterol Hepatol.13:2312-9).

Once a mammal is identified as being responsive to one or moreinterventions (e.g., pharmacological intervention, surgicalintervention, weight loss device, diet intervention, behaviorintervention, and/or microbiome intervention) based, at least in part,on an obesity phenotype, which is based, at least in part, on an obesityanalyte signature in the sample, the mammal can be administered orinstructed to self-administer one or more individualizedpharmacotherapies.

When a mammal is identified as having an obesity phenotype that isresponsive to treatment with one or more pharmacotherapies, the mammalcan be administered or instructed to self-administer one or morepharmacotherapies. For example, when a mammal is identified as having alow satiation (Group 1) phenotype, based, at least in part, on anobesity analyte signature, the mammal can be administered or instructedto self-administer phentermine-topiramate (e.g., phentermine-topiramateER) to treat the obesity. For example, when a mammal is identified ashaving a low satiation (Group 1) phenotype, based, at least in part, onan obesity analyte signature, the mammal can be administered orinstructed to self-administer lorcaserin to treat the obesity. Forexample, when a mammal is identified as having a low satiety (Group 2)phenotype, based, at least in part, on an obesity analyte signature, themammal can be administered or instructed to self-administer liraglutideto treat the obesity. For example, when a mammal is identified as havinga behavioral eating (Group 3) phenotype, based, at least in part, on anobesity analyte signature, the mammal can be administered or instructedto self-administer naltrexone-bupropion (e.g., naltrexone-bupropion SR)to treat the obesity. For example, when a mammal is identified as havinga large fasting gastric volume (Group 4) phenotype, based, at least inpart, on an obesity analyte signature, the mammal can be administered orinstructed to self-administer naltrexone-bupropion (e.g.,naltrexone-bupropion SR) to treat the obesity. For example, when amammal is identified as having a low resting energy expenditure (Group6) phenotype, based, at least in part, on an obesity analyte signature,the mammal can be administered or instructed to self-administerphentermine, and can be instructed to increase physical activity totreat the obesity.

In some cases, one or more pharmacotherapies described herein can beadministered to an obese mammal as a combination therapy with one ormore additional agents/therapies used to treat obesity. For example, acombination therapy used to treat an obese mammal (e.g., an obese human)can include administering to the mammal one or more pharmacotherapiesdescribed herein and one or more obesity treatments such as weight-losssurgeries (e.g., gastric bypass surgery, laparoscopic adjustable gastricbanding (LAGB), biliopancreatic diversion with duodenal switch, and agastric sleeve), vagal nerve blockade, endoscopic devices (e.g.intragastric balloons or endoliners, magnets), endoscopic sleevegastroplasty, and/or gastric or duodenal ablations. For example, acombination therapy used to treat an obese mammal (e.g., an obese human)can include administering to the mammal one or more pharmacotherapiesdescribed herein and one or more obesity therapies such as exercisemodifications (e.g., increased physical activity), dietary modifications(e.g., reduced-calorie diet), behavioral modifications, commercialweight loss programs, wellness programs, and/or wellness devices (e.g.dietary tracking devices and/or physical activity tracking devices). Incases where one or more pharmacotherapies described herein are used incombination with one or more additional agents/therapies used to treatobesity, the one or more additional agents/therapies used to treatobesity can be administered/performed at the same time or independently.For example, the one or more pharmacotherapies described herein can beadministered first, and the one or more additional agents/therapies usedto treat obesity can be administered/performed second, or vice versa.

This document provides methods and materials for identifying one or moreanalytes associated with obesity. In some cases, analytes associatedwith obesity can be used in an obesity analyte signature as describedherein. For example, one or more analytes associated with obesity can beidentified by using a combined logit regression model. In some cases, acombined logit regression model can include stepwise variable selection(e.g., to identify variables significantly associated with a specificobesity phenotype). For example, one or more analytes associated withobesity can be identified as described in, for example, the Examplessection provided herein.

The invention will be further described in the following examples, whichdo not limit the scope of the invention described in the claims.

EXAMPLES Example 1: Identification of Obesity Biomarkers

Obesity phenotypes were associated with higher BMI, distinguish obesityphenotypes, and can be used to predict responsiveness to obesitypharmacotherapy and endoscopic devices (see, e.g., Acosta et al., 2015Gastroenterology 148:537-546). In this study, biomarkers specific toeach obesity phenotype were identified using metabolomics.

The overall cohort demographics [median (IQR)] were age 36 (28-46)years, BMI 35 (32-38) kg/m², 75% females, 100% Caucasians. The groupsbased on phenotype > or <75% ile were not statistically different forbody weight, waist circumference, hip circumference, fasting glucose.The group distribution in this cohort was: abnormal satiation (16%),abnormal satiety (16%), abnormal hedonic/psych (19%), slowmetabolism/energy expenditure (32%), and mixed group (17%) (FIG. 1A).FIGS. 1B-E illustrate summarize characteristics of the quantitativechanges in the subgroups: the satiation group consumed 591 (60%) morecalories prior to reaching fullness; the satiety group emptied half ofthe solid 300 kcal meal 34 min (30%) faster; the hedonic group reported2.8 times higher levels of anxiety; the slow metabolism group has 10%decreased predicted resting energy expenditure than other groups. Theseaverage differences were in comparison to the other groups, butexcluding the group with participants with a mixed or overlappingphenotype.

Gastrointestinal Traits (Phenotypes) Associated with Obesity

Gastrointestinal functions, satiation, and satiety were characterized in509 participants across the normal weight to obesity spectrum. Obesitywas associated with decreased satiation (higher caloric intake beforefeeling full, measure by volume to fullness [VTF] p=0.038), largefasting gastric volume (GV, p=0.03), accelerated gastric emptying (GE)T_(1/2) (solids: p<0.001; liquids: p=0.011), and lower postprandial peakplasma levels of PYY (p=0.003). In addition, principal components (PC)analysis identified latent dimensions (LDs) accounting for −81% of OW-OBvariation and sub-classifies obesity in satiation (21%), gastriccapacity (15%), behavioral (13%), gastric sensorimotor (11%) factors,GLP-1 levels (9%), and others (31%) (Acosta et al., 2015Gastroenterology 148:537-546).

Identification of Biomarkers

An analysis of 102 patients with obesity, matched for gender, age andBMI was done. These individuals were non-diabetic and were in notmedications for weight loss. Based on the profile of each patient wewere able to validate the main groups in obesity in 1) low satiation, 2)rapid return to hunger, 3) behavioral eating (identified byquestionnaire), 4) large fasting gastric volume, 5) mixed, and 6) lowresting energy expenditure group.

A combined logit regression model using stepwise variable selection wascreated to identify variables that are significantly associated witheach of the phenotypic classes. Untargeted metabolomics identifiedunique metabolites in each group (FIG. 2A). Each of these metabolites isindependent from the other groups (FIG. 2B). From these metabolites, a“VIP” (variable of importance) was identified for each group. Then, atargeted metabolomics was done with the VIP as well asneurotransmitters, amino compounds, fatty acids, and short chain fattyacids. Examples variables are as shown in Tables 2-5. For example,targeted metabolites, peptides, and SNPS analyzed are as shown in Table2, other obesity related gene variants are as shown in in Table 3,targeted peptides are as shown in in Table 4, and targeted genes are asshown in in Table 5.

TABLE 2 Analytes Examined using SNPs, Hormones, Peptides and TargetedMetabolomics. Fatty SNP- Neuro- acids and Amino Bile containingtransmitters Lipid Compounds acids Peptides Genes Hormones carbohydratesγ-aminobutyric acetic Histidine LCA ghreIin ADRA2A cholesterol glucoseacid AcetyIchoIine propionic HydroxyproIine CDCA PYY ADRA2C insulinAdenosine isobutyric 1-MethyIhistidine DCA CCK GNB3 glucagon aspartatebutyric 3-MethyIhistidine UDCA GLP-1 FTO leptin Dopamine vaIericAsparagine HDCA GLP-2 MC4R adiponectin D-serine isovaIericPhosphoethanoIamine CA gIucagon TCF7L2 GIutamate hexanoic Arginine GLCAoxyntomoduIin 5-HTTLPR GIycine octanoic Carnosine GCDCA neurotensinHTR2C Histidine myristic Taurine GDCA FGF UCP2 Norepinephrine paImiticAnserine GUDCA GIP UCP3 Serotonin paImitoleic Serine GHDCA OXM GPBAR1Taurine paImiteIaidic GIutamine GCA FGF19 NR1H4 stearic EthanoIamineTLCA FGF21 FGFR4 oIeic GIycine TCDCA LDL PYY eIaidic Aspartic Acid TDCAinsuIin GLP-1 IinoIeic Sarcosine TUDCA glucagon CCK a-IinoIenic ProIineTHDCA amylin Leptin arachidonic aIpha- TCA pancreatic AdiponectinAminoadipic- polypeptide acid eicosapentaenoic beta- DHCA leptinNeurotensin Aminoisobutyric- acid docosahexaenoic alpha-Amino- THCAadiponectin GhreIin N-butyric-acid LDL Ornithine GLP-1 receptortriglycerides Cystathionine 1 GOAT Cystine DPP4 Lysine TyrosineMethionine VaIine IsoIeucine Leucine Homocystine PhenyIaIanineTryptophan CitruIIine GIutamic Acid beta-AIanine Threonine AlanineHydroxyIysine 1 Acetone Acetoacetic Acid

TABLE 3 SNPs associated with obesity [Is this title accurate?[ SNP Chr.Position (bp) Nearest Genes rs657452 1 49,362,434 AGBL4 rs11583200 150,332,407 ELAVL4 rs2820292 1 200,050,910 NAV1 rs11126666 2 26,782,315KCNK3 rs11688816 2 62,906,552 EHBP1 rs1528435 2 181,259,207 UBE2E3rs7599312 2 213,121,476 ERBB4 rs6804842 3 25,081,441 RARB rs2365389 361,211,502 FHIT rs3849570 3 81,874,802 GBE1 rs16851483 3 142,758,126RASA2 rs17001654 4 77,348,592 SCARB2 rs11727676 4 145,878,514 HHIPrs2033529 6 40,456,631 TDRG1 rs9400239 6 109,084,356 FOXO3 rs13191362 6162,953,340 PARK2 rs1167827 7 75,001,105 HIP1 rs2245368 7 76,446,079DTX2P1 rs2033732 8 85,242,264 RALYL rs4740619 9 15,624,326 C9orf93rs6477694 9 110,972,163 EPB41L4B rs1928295 9 119,418,304 TLR4 rs107336829 128,500,735 LMX1B rs7899106 10 87,400,884 GRID1 rs17094222 10102,385,430 HIF1AN rs11191560 10 104,859,028 NT5C2 rs7903146 10114,748,339 TCF7L2 rs2176598 11 43,820,854 HSD17B12 rs12286929 11114,527,614 CADM1 rs11057405 12 121,347,850 CLIP1 rs10132280 1424,998,019 STXBP6 rs12885454 14 28,806,589 PRKD1 rs3736485 15 49,535,902DMXL2 rs758747 16 3,567,359 NLRC3 rs2650492 16 28,240,912 SBK1 rs992596416 31,037,396 KAT8 rs1000940 17 5,223,976 RABEP1 rs1808579 18 19,358,886C18orf8 rs7243357 18 55,034,299 GRP rs17724992 19 18,315,825 PGPEP1rs977747 1 47,457,264 TAL1 rs1460676 2 164,275,935 FIGN rs17203016 2207,963,763 CREB1 rs13201877 6 137,717,234 IFNGR1 rs1441264 1378,478,920 MIR548A2 rs7164727 15 70,881,044 LOC100287559 rs2080454 1647,620,091 CBLN1 rs9914578 17 1,951,886 SMG6 rs2836754 21 39,213,610ETS2 rs492400 2 219,057,996 USP37 rs16907751 8 81,538,012 ZBTB10rs9374842 6 120,227,364 LOC285762 rs9641123 7 93,035,668 CALCR rs954049313 65,103,705 MIR548X2 rs4787491 16 29,922,838 INO80E rs6465468 795,007,450 ASB4 rs7239883 18 38,401,669 LOC284260 rs3101336 1 72,523,773NEGR1 rs12566985 1 74,774,781 FPGT rs12401738 1 78,219,349 FUBP1rs11165643 1 96,696,685 PTBP2 rs17024393 1 109,956,211 GNAT2 rs543874 1176,156,103 SEC16B rs13021737 2 622,348 TMEM18 rs10182181 2 25,003,800ADCY3 rs1016287 2 59,159,129 LINC01122 rs2121279 2 142,759,755 LRP1Brs13078960 3 85,890,280 CADM2 rs1516725 3 187,306,698 ETV5 rs10938397 444,877,284 GNPDA2 rs13107325 4 103,407,732 SLC39A8 rs2112347 575,050,998 POC5 rs205262 6 34,671,142 C6orf106 rs2207139 6 50,953,449TFAP2B rs17405819 8 76,969,139 HNF4G rs10968576 9 28,404,339 LINGO2rs4256980 11 8,630,515 TRIM66 rs11030104 11 27,641,093 BDNF rs3817334 1147,607,569 MTCH2 rs7138803 12 48,533,735 BCDIN3D rs12016871 1326,915,782 MTIF3 rs12429545 13 53,000,207 OLFM4 rs11847697 14 29,584,863PRKD1 rs7141420 14 78,969,207 NRXN3 rs16951275 15 65,864,222 MAP2K5rs12446632 16 19,842,890 GPRC5B rs3888190 16 28,796,987 ATP2A1 rs155890216 52,361,075 FTO rs12940622 17 76,230,166 RPTOR rs6567160 18 55,980,115MC4R rs29941 19 39,001,372 KCTD15 rs2075650 19 50,087,459 TOMM40rs2287019 19 50,894,012 QPCTL rs3810291 19 52,260,843 ZC3H4 rs7715256 5153,518,086 GALNT10 rs2176040 2 226,801,046 LOC646736 rs6091540 2050,521,269 ZFP64 SNP Chr. Position (bp) Genes rs1800544 ADRA2AIns-Del-322 ADRA2C rs5443 GNB3 rs1129649 GNB3 rs1047776 GNB3 rs9939609FTO rs17782313 MC4R rs7903146 TCF7L2 rs4795541 5-HTTLPR rs3813929 HTR2Crs518147 HTR2C rs1414334 HTR2C rs659366 UCP2 −3474, UCP2 rs2075577 UCP3rs15763 UCP3 rs1626521 UCP3 rs11554825 GPBAR1 rs4764980 NR1H4 rs434434FGFR4 rs351855 FGFR4 RSID Gene_Symbol exm2261885 . exm2264702 .kgp10003923 . kgp10360658 . kgp10374580 . kgp1093561 . kgp11089754 .kgp11154375 . kgp11564777 . kgp11808957 . kgp11836456 . kgp11902597 .SNP Chr. Position (bp) Nearest Genes kgp12031075 z z . kgp12088423 .kgp1283935 . kgp1287405 . kgp129784 . kgp1371036 . kgp1419661 .kgp1612367 . kgp16387096 . kgp16914214 . kgp2241756 . kgp2251945 .kgp238191 . kgp2727759 . kgp2735253 . kgp2925720 . kgp3186084 .kgp3371090 . kgp3712407 . kgp3846165 . kgp3847753 . kgp429141 .kgp4433253 . kgp447667 . kgp4725781 . kgp5201059 . kgp5201171 .kgp5269120 . kgp5471252 . kgp5829795 . kgp599811 . kgp6037240 .kgp6508014 . kgp6615769 . kgp6816777 . kgp7069937 . kgp7157564 .kgp7328604 . kgp7496475 . kgp7707096 . kgp7798504 . kgp8018963 .kgp8206543 . kgp8818851 . kgp8860587 . kgp9190754 . kgp9456377 .kgp9526272 . kgp9629679 . rs10489944 . rs10504589 . rs10808295 .rs11060968 . rs11225943 . rs11720464 . rs12354667 . rs12427263 .rs13130205 . rs1372851 . rs1493716 . rs1541616 . rs1674070 . rs1873367 .rs1889757 . rs2470000 . rs2470029 . rs2647979 . rs2720400 . rs2851820 .rs2851836 . rs288756 . rs348337 . rs4707490 . rs6005420 . rs6008618 .rs6472339 . rs6776731 . rs6828992 . rs6888630 . rs6957234 . rs7082638 .rs7297442 . rs7658020 . rs7803317 . rs8141901 . rs849309 . rs9511655 .rs9810198 . rs9860734 . exm2264762 . exm2271737 . exm2272553 .kgp10285805 . kgp10360658 . kgp10548537 . kgp10901790 . kgp11044637 .kgp11343144 . kgp11430653 . kgp11530429 . kgp11836456 . kgp11960081 .kgp11974172 . kgp12031075 . kgp12088423 . kgp12289889 . kgp127695 .kgp1278486 . kgp1586406 . kgp16387096 . kgp1727603 . kgp1887803 .kgp1939387 . kgp2241672 . kgp22776953 . kgp227938 . kgp2369570 .kgp3371090 . kgp3406296 . kgp3660486 . kgp3662728 . kgp3712407 .kgp374568 . kgp3846165 . kgp4074864 . kgp429141 . kgp447667 . kgp4534617. kgp4799975 . kgp4944907 . kgp5201171 . kgp5329941 . kgp5471252 .kgp563498 . kgp5671927 . kgp5780899 . kgp5829795 . kgp6037240 .kgp6688816 . kgp6827318 . kgp7048855 . kgp7069937 . kgp7235499 .kgp7945681 . kgp8628976 . kgp8860587 . kgp9190754 . kgp9231149 .kgp9578092 . kgp965777 . rs10150519 . rs10161070 . rs10451103 .rs10742039 . rs10877143 . rs11720464 . rs12506204 . rs12593784 .rs12937299 . rs13338004 . rs1372851 . rs1493716 . rs1512840 . rs1541616. rs16822391 . rs17076260 . rs17453871 . rs1873367 . rs201607 . rs202558. rs2169564 . rs2647979 . rs2720400 . rs2761413 . rs2957787 . rs3762535. rs4313958 . rs4461665 . rs4543516 . rs4707490 . rs4964150 . rs6008618. rs6448182 . rs6585563 . rs6828992 . rs6957234 . rs7297442 . rs7658020. rs768969 . rs7846145 . rs7981554 . rs8002390 . rs8141901 . rs935201 .rs9378848 . rs9810198 . rs9860734 . rs9864846 . kgp2297621 ACSL6rs440970 ACSL6 kgp10461170 ADARB2 kgp7332119 ADARB2 rs12415114 ADARB2kgp9064589 ADCY8 rs13133908 ADH1B kgp12414761 ADH1B rs13133908 ADH1Brs2075633 ADH1B rs7518469 AJAP1 rs429790 ATP2C2 kgp3288649 ATP6V0D2rs2832231 C21orf7 kgp10136381 CAMKMT kgp3161157 CAMKMT kgp3203202 CAMKMTkgp3968222 CAMKMT kgp4140267 CAMKMT rs13406580 CAMKMT rs1551882 CAMKMTrs17032193 CAMKMT rs7593926 CAMKMT kgp4005992 CAP2 kgp7298922 CASC4kgp1789974 CD48 kgp5511006 CD48 kgp1789974 CD48 kgp5511006 CD48kgp7256435 CDC42SE2 rs4706020 CDC42SE2 rs3812178 CDYL rs3812179 CDYLkgp3731792 CES5AP1 kgp6395031 CLMN kgp6395031 CLMN kgp4873414 CNPY4rs3806043 COL19A1 kgp3071123 COL27A1 kgp6064462 COL27A1 kgp6796371COL27A1 rs1249745 COL27A1 kgp5314602 COL4A3 kgp11506369 CORO1Ckgp6473219 CORO1C kgp10992142 CPZ rs8087866 CTIF kgp7710562 DAAM2exm430196 DCHS2 exm430197 DCHS2 kgp49288 DCHS2 kgp766527 DCHS2 rs4696584DCHS2 kgp3890072 DOCK8 rs1980876 DOCK8 exm451231 EGFLAM rs6897179 EGFLAMkgp10622968 FAM125B kgp5732367 FAM71E2 kgp10294313 FRMD3 kgp2344514FRMD3 kgp7900743 FRMD3 kgp3392580 GALNTL4 kgp4456104 GLT1D1 kgp5287249HHAT exm1319778 KRT23 rs8037 KRT23 rs9257 KRT23 kgp10012744 LHPPkgp1057196 LINC00578 kgp8853148 LINC00578 rs6799682 LINC00578 rs7632844LINC00578 kgp11567842 LINC00620 rs12495328 LINC00620 kgp4159029 LIPCkgp1640513 LOC100128714 kgp4598936 LOC100128714 kgp5262759 LOC100128714rs11635697 LOC100128714 rs12593847 LOC100128714 rs8023270 LOC100128714kgp1743339 LOC100287160 kgp7667092 LOC100289473 rs6135960 LOC100289473rs6135960 LOC100289473 kgp5351206 LOC100293612|LINC00620 kgp10995216LOC100506869 kgp22804264 LOC100506869 rs4760137 LOC100506869 rs1566141LOC100507053 kgp10134243 LOC100507053 rs10008281 LOC100507053 rs1229966LOC100507053 rs1566141 LOC100507053 rs2051428 LOC100507053 rs3819197LOC100507053|ADH1A rs3819197 LOC100507053|ADH1A rs9995799LOC100507053|ADH6 kgp1289034 LOC100507443 rs9981988 LOC100996571|CYYR1kgp258053 LOC152225 kgp6272649 LOC152225 kgp2759189 LOC255130 kgp2759189LOC255130 rs10980642 LPAR1 kgp5423754 LUZP2 kgp8988372 LUZP2 kgp9462081MCM7 rs2261360 MCM7 kgp8065051 MICAL3 kgp4439669 MMS19 kgp18459 MYBPC1kgp1800707 NR2F2-AS1 rs7831515 NSMCE2 rs16958048 NTN1 kgp7166603 OAZ2kgp7077044 O3FAR1 kgp2414524 OSBP2 kgp2580452 OSBP2 kgp7020841 OSBP2rs4820897 OSBP2 kgp7020841 OSBP2 kgp7082195 P4HA2 rs6667138 PADl1rs6667138 PADll kgp7908292 PARD3B rs7558785 PARD3B kgp11077304 PARK2exm-rs2795918 PCDH15 kgp1058322 PCDH15 kgp11029138 PCDH15 kgp11410092PCDH15 kgp2961930 PCDH15 kgp5544438 PCDH15 kgp9631691 PCDH15 rs4082042PCDH15 kgp9916431 PK1B rs13218313 PK1B kgp4769029 PRH1-PRR4 kgp9716281PTPRD rs7045790 RALGPS1|ANGPTL2 rs17081778 RPS24P10 kgp10074267 RTN4RL1kgp11049623 RYR2 kgp8225782 SCN2A rs2075703 SCN2A rs6744911 SCN2Ars12706974 SEMA3C rs1358340 SEMA3C kgp7788385 SEMA5A rs3822799 SEMA5Akgp3608544 SFMBT2 rs1887757 SGCG rs9580573 SGCG kgp390881 SLC22A15kgp2776219 SLC2A2 exm988933 SLCO1B1 rs2306283 SLCO1B1 rs4149040 SLCO1B1exm988933 SLCO1B1 rs2306283 SLCO1B1 rs4149040 SLCO1B1 kgp8338369 SMOC2kgp12080543 SNCA1P kgp4607090 SNCA1P rs4895350 SNCA1P kgp1043980 SNX18kgp2071353 SRRM4 kgp7567091 SUSD1 kgp7567091 SUSD1 kgp12526521 TBC1D16kgp5557307 TCERG1L rs13340295 TENM3 kgp3414710 TJP3 kgp10345778 TLL1rs4690833 TLL1 rs2568085 TMEM9B rs1071646 TPM1 rs6738 TPM1 rs1071646TPM1 rs6738 TPM1 rs1408817 VTl1A rs216905 VWF kgp2039705 WWOX rs6804325WWTR1 exm382632 ZFYVE28 rs12532238 ZNF3 rs6592 ZNF3 kgp6175568 ZNF609rs11558476 ZSCAN21

TABLE 4 Gastrointestinal peptides associated with obesity. HormoneSource Normal function Cholecystokinin (CCK) Duodenum lncrease satiationGhrelin Gastric fundus Stimulate appetite Glucagon-like Distal smalllncrease satiety peptide 1 (GLP-1) intestine and colon Peptide YY (PYY)Distal small lncrease satiety intestine and colon * Low calorie diet canalter peptide concentrations.

TABLE 5 Genes associated with obesity. Genotype potentially affectingorgan or Primary mechanisms associated Endpoints Quantitative Trait withtrait Satiety Postprandial GLP-1 TCF7L2, GNB3, MC4R and PYY SatiationVTF, MTV (kcal) MC4R, GNB3, HTR2C, UCP3 Gastric GE (solids) TCF7L2,ADRA2A, UCP3 Emptying Appetite Fasting Ghrelin MC4R, FTO, GNB3 * Genevariants were selected based on association with BMI and mechanism ofaction.

Table 6 summarizes the variables that were significantly associated witheach of the phenotypic groups vs the rest of the groups.

TABLE 6 SNPs present in each obesity group Obesity Exemplary PhenotypeGroup Gene SNP 1: low satiation HTR2C, POMC, NPY, rs1414334 AGRP, MC4R,GNB3, SERT, BDNF 2: low satiety PYY, GLP-1, MC4R, rs7903146 GPBAR1,TCF7L2, ADRA2A, PCSK, TMEM18 3: behavioral eating SLC6A4/SERT, DRD2rs4795541 4: large fasting gastric volume TCF7L2, UCP3, rs1626521ADRA2A, 5: mixed 6: low resting energy expenditure FTO, LEP, LEPR,rs2075577 UCP1, UCP2, UCP3, ADRA2, KLF14, NPC1, LYPLAL1, ADRB2, ADRB3,BBS1

Combinations of compounds (amino-compounds, neurotransmitters fattyacids, metabolic peptides, and metabolic gene) were identified assignificantly associated with each of the obesity phenotypic groups. Thevariables that were significantly associated with each of the phenotypicgroups included the following:

Questionnaire results:

-   -   hospital anxiety and depression scale—anxiety subscale (HADS-A),

Metabolites:

-   -   1-methylhistine    -   seratonin    -   glutamine    -   gamma-amino-n-butyric-acid    -   isocaproic    -   allo-isoleucine    -   hydroxyproline    -   beta-aminoisobutyric-acid    -   alanine    -   hexanoic    -   tyrosine    -   phenylalanine

Gastrointestinal Peptides:

-   -   fasting ghrelin    -   fasting PYY

Algorithm

The following formulas were used to identify the obesity phenotype of apatient based upon the signature of the 14 compounds identified as beingsignificantly associated with each of the obesity phenotypic groups. Theformulas predicted the phenotypes with a r2 of 0.90 and a probabilityChi-square of less than 0.0001.

Lin[1[

-   -   (−1552.38148595936)    -   +40.797700201235*:Name(“HADS-A”)    -   +1.32549623006262*:Glutamine    -   +−0.111622239757052*:Alanine    -   +−616.954862561479*:Name(“gamma-Amino-N-butyric-acid”)    -   +89.402967640225*:Name(“beta-Aminoisobutyric-acid”)    -   +1.73891527871898*:Tyrosine    -   +6.24138513457712*:Phenylalanine    -   +2148.66822848398*:isocaproic    -   +−20.6187102527618*:hexanoic    -   +56.3110714341266*:Name(“Log(Hydravproline)”)    -   +82.3818646650792*:Name(“Log(1-Methylhistine)”)    -   +26.8826686365131*:Name(“Log(seratonin)”)    -   +0.245926705626903*:Name(“PYY_-15”)    -   +1.89180999803712*:Name(“Ghrelin_-15”)    -   +75.8755521857061*:Name(“allo-Isoleucine”)

Lin [2[

-   -   (−2031.26556804871)    -   +56.9736558824775*:Name(“HADS-A”)    -   +0.0118072070103887*:Glutamine    -   +−0.0995668418558728*:Alanine    -   +1609.83650774629*:Name(“gamma-Amino-N-butyric-acid”)    -   +123.106026249695*:Name(“beta-Aminoisobutyric-acid”)    -   +13.0377088181536*:Tyrosine    -   +−2.42979784589652*:Phenylalanine    -   +3057.74326808551*:isocaproic    -   +63.6119366218627*:hexanoic    -   +99.2853520251878*:Name(“Log(Hydroxyproline)”)    -   +0.166314503531418*:Name(“Log(1-Methylhistine)”)    -   +6.21451740476229*:Name(“Log(seratonin)”)    -   +0.696742681406157*:Name(“PYY_-15”)    -   +2.30188885859994*:Name(“Ghrelin_-15”)    -   +220.083419205279*:Name(“allo-Isoleucine”)

Lin [3[

-   -   (−735.067323742327)    -   +84.6709055694921*:Name(“HADS-A”)    -   +0.739638607406857*:Glutamine    -   +0.0161670919675227*:Alanine    -   +1.70702352345921*:Name(“gamma-Amino-N-butyric-acid”)    -   +4.08385430756663*:Name(“beta-Aminoisobutyric-acid”)    -   +4.83658065569896*:Tyrosine    -   +−7.4973831454893*:Phenylalanine    -   +1467.49860590747*:isocaproic    -   +51.4109043756237*:hexanoic    -   +−56.3364437814115*:Name(“Log(Hydroxyproline)”)    -   +45.3693267895892*:Name(“Log(1-Methylhistine)”)    -   +24.1167481430051*:Name(“Log(seratonin)”)    -   +1.56458536889981*:Name(“PYY_-15”)    -   +2.15880622406247*:Name(“Ghrelin_-15”)    -   +72.4632042822316*:Name(“allo-Isoleucine”)

Lin[4[

-   -   (−38.8679541168302)    -   +1.54112014174663*:Name(“HADS-A”)    -   +0.00119976598048842*:Glutamine    -   +0.056518755537321*:Alanine    -   +34.9734228686154*:Name(“gamma-Amino-N-butyric-acid”)    -   +2.64367056830481*:Name(“beta-Aminoisobutyric-acid”)    -   +0.0996495148185086*:Tyrosine    -   +−0.14869421223421*:Phenylalanine    -   +8.69300091428836*:isocaproic    -   +1.77363291550863*:hexanoic    -   +−1.58953123143685*:Name(“Log(Hydroxyproline)”)    -   +0.127307799711255*:Name(“Log(1-Methylhistine)”)    -   +3.33170879355105*:Name(“Log(seratonin)”)    -   +0.0387731073018872*:Name(“PYY_-15”)    -   +0.0662851699121999*:Name(“Ghrelin_-15”)    -   +1.4086102207227*:Name(“allo-Isoleucine”)

1/(1+Exp(−(“Lin[1]”))+Exp((“Lin[2]”)−(“Lin[1]”))+Exp((“Lin[3]”)−(“Lin[1]”))+Exp((“Lin[4]”)−(“Lin[1]”)))  Prob[1[

1/(1+Exp((“Lin[1]”)−(“Lin[2]”))+Exp(−(“Lin[2]”))+Exp((“Lin[3]”)−(“Lin[2]”))+Exp((“Lin[4]”)−(“Lin[2]”)))  Prob[2[

1(1+Exp(“Lin[1]”)−(“Lin[3]”))+Exp((“Lin[2]”)−(“Lin[3]”))+Exp(−(“Lin[3]”))+Exp((“Lin[4]”)−(“Lin[3]”)))  Prob[3[

1/(1+Exp((“Lin[1]”)−(“Lin[4]”))+Exp((“Lin[2]”)−(“Lin[4]”))+Exp((“Lin[3]”)−(“Lin[4]”))+Exp(−(“Lin[4]”)))  Prob[4 [

1/(1+Exp((“Lin[1]”))+Exp((“Lin[2]”))+Exp((“Lin[3]”))+Exp((“Lin[4]”)))  Prob[6 [

Table 7 summarizes variables (14 analytes and a questionnaire) that weresignificantly associated with each of the phenotypic groups with the ROCof the group vs the rest of the groups.

TABLE 7 Compounds present in each obesity group Source Group 1 Group 2Group 3 Group 4 Group 5 Group 6 1-Methylhistine + + + seratonin + + + +Glutamine + + + gamma-Amino-N-butyric-acid + + isocaproic + + +allo-lsoleucine + + + + Hydroxyproline + +beta-Aminoisobutyric-acid + + + + Alanine + + + + + hexanoic + + +Tyrosine + + + Phenylalanine + + + Ghrelin + PYY + + + HADS-A + + + + pvalue of whole model test 0.006 <0.001 <0.001 <0.001 0.01 <0.001 ROCvalue (group vs rest) 0.90 0.86 0.91 0.89 0.86 0.96

One multinomial logistic model contained 14 compounds and onequestionnaire, and the obesity phenotypes were predicted with more than97% sensitivity and specificity (group 1=1, group 2=1, group 3=1, group4=0.97 and group 6=0.96). When a mixed group is added to the equation,the obesity phenotypes can be predicted with more than 91% sensitivityand specificity (group 1=0.95, group 2=0.92, group 3=1, group 4=0.96,group 5=0.96 and group 6=0.97). When group 4 and mixed are removed fromthe equation, the obesity phenotypes can be predicted with 100%sensitivity and specificity.

Another multinomial logistic model contained 1 behavioral assessment, 3germline variants, and 6 fasting targeted metabolomics. The variablescan be as shown in Table 8 plus questionnaire(s) (e.g., HADS and/orTEFQ21).

TABLE 8 Variables (9 analytes and a questionnaire) that weresignificantly associated with each of the phenotypic groups with the ROCof the group vs the rest of the groups. SNP Gene Name Panel Peptiders9939609.n FTO cystathionine1 amino compound fasting pyy rs1626521.nUCP3 glycine amino compound fasting ghrelin rs3813929.n 5-HT2CR valineamino compound rs5443.n GNB3 serotonin neurotransmitter rs1800544.nADRA2a glutamic acid amino compound rs2234888.n ADRA2c tryptophan aminocompound rs17782313 MC4R histidine amino compound methylhistidine1 aminocompound methionine amino compound isocaproic amino compoundhydroxylysine2 amino compound ethanolamine amino compound hydroxyprolineamino compound gamma-amino- neurotransmitter n-butyric acid threonineamino compound alpha- amino compound aminoadipic acid sarcosine aminocompound arginine amino compound histidine neurotransmitter prolineamino compound

Obesity Phenotypes Biomarker

Simple-blood test biomarkers were identified that can classify obesepatients into their related phenotypes. To achieve this, 25 individualswith unique obesity phenotypes were selected from the cohort of 180participants and an untargeted metabolomics study was performed usingtheir fasting blood samples. Thus, average of 3331 unique metabolitesthat are associated with each obesity-related phenotype were observedand this is illustrated through the VennDiagrams of Unique Metabolitesper group using Positive-HILIC Untargeted Metabolomics (FIG. 2A). Thesedata supported the application of a targeted metabolomics approach,hypothesis-driven, to identify and quantify associated metabolites. Atwo-stage design was used to develop the composition of the blood test;the training and validation cohorts consisted of 102 and 78 obesepatients, respectively. Based on the profile of each patient, we wereable to validate the main groups in obesity cohorts, that is 1) abnormalsatiation, 2) rapid return to hunger, 3) behavioral eating; 4) abnormalenergy expenditure; 5) a “mixed” group. Using a multinomial logisticregression was used to develop a classification model using elastic netshrinkage for variable selection. Discrimination was evaluated usingconcordance index (c-index). Receiving operating characteristic curves(ROC) for the models were constructed and area under the curve (AUC)estimated. The variables were applied to a prediction model or algorithmdiagnostic (patent submitted) to identify the phenotypes. The modelpredicted the phenotypes with an ROC of 0.91 (AUC) for the trainingcohort and 0.71 for the validation cohort (FIG. 3 and FIG. 5 ). Theaccuracy of the model was 86% in the whole cohort. When the model wasapplied to the two previously completed placebo-controlled, randomizedtrials, the weight loss after 2 weeks of phentermine-topiramate ER was58% higher in the predicted group (n=3, 2.4±0.4 kg) compared to theother groups (n=9, 1.4±0.2 kg), and after 4 weeks of exenatide, weightloss was 65% higher in the predicted group (n=6; 1.5±0.6 kg) compared tothe other groups (n=4, 0.9±0.7 kg).

In summary, using this actionable classification decreases obesityheterogeneity, and facilitate our understanding of human obesity.Furthermore, we have developed and validated a novel, first-of-its-kind,simple, fasting, blood-based biomarker for obesity phenotypes.

Sensitivity and Specificity of Biomarkers

To confirm the sensitivity and specificity of the biomarkerssignificantly associated with each of the obesity phenotypic groups, areceiver operating characteristic (ROC) analysis was done.

FIG. 3 shows the sub-classification prediction accuracy of this combinedmodel and an ROC analysis showed that this model has >0.90 area underthe curve (AUC) for all six classes.

Next, binary classification models were derived that can predict whethera patient belongs to one group over the others. Bayesian covariatepredictors were derived for low satiation, behavioral eating, and lowresting energy expenditure. These models yielded an ROC AUC of 1 (FIG. 4). These data suggested that the serum metabolite levels hold all theinformation needed to predict obesity subclasses.

Validation of Biomarkers

To further validate the ability to phenotype obesity based on variablessignificantly associated with each of the phenotypic groups, the formulawas applied to 60 new participants with obesity, and a ROC analysis wasdone.

FIG. 5 shows that the formula predicted the sub-groups with over 90%sensitivity and specificity.

Summary

These results demonstrate that serum biomarkers can be used to classifyobesity patients into obesity phenotype groups.

Example 2: Obesity Phenotypes and Intervention Responsiveness

Obesity is a chronic, relapsing, multifactorial, heterogeneous disease.The heterogeneity within obesity is most evident when assessingtreatment response to obesity interventions, which are generallyselected based on BMI. These standard approaches fail to address theheterogeneity of obesity. As described in Example 1, obesity phenotypeswere associated with higher BMI, distinguish obesity phenotypes. ThisExample shows that obesity phenotypes respond differently to specificinterventions (e.g., pharmacological interventions). Obesity-relatedphenotypes were evaluated to facilitate the understanding of obesitypathophysiology, and identify sub-groups within the complex andheterogeneous obese population. A novel classification based onidentifying actionable traits in the brain-gut axis in humans (see,e.g., Acosta et al., 2015 Gastroenterology 148:537-546 e534; andCamilleri et al., 2016 Gastrointest Endosc. 83:48-56) was applied tounderstand, in a more homogenous, phenotype-defined population, theunique or specific characteristics within each sub-group of obesity.

The specific characteristics of 180 participants with obesity (definedas BMI>30 kg/m²) were grouped based on their predominant obesity-relatedphenotype, based on a multiple step process (in addition to gender) togenerate a homogeneous populations based on the 75^(th) percentilewithin the obese group for each well-validated variable: a) satiation[studied by nutrient drink test (maximal tolerated volume, 1 kcal/ml)],b) satiety [studied by gastric emptying (T_(1/2), min)], c) hedonic(hospital anxiety and depression score [HADS] questionnaire), d) other(none of the above) and e) mixed (two or more criteria met).

The overall cohort demographics were as described in Example 1. Then,with the intention to validate further the applicability of the obesityphenotypes, the fact that each sub-group may have unique abnormalitiescompared to the other groups when tested with previously validated orreported findings in common obesity was interrogated.

Abnormal Satiation Group

Individuals with obesity typically consume more calories prior to reach‘usual’ fullness—for every 5 kg/m² of BMI increase, participantsconsumed 50 calories more (see, e.g., Acosta et al., 2015Gastroenterology 148:537-546). Here, participants with obesity andabnormal satiation were compared to the other groups with the samevalidated two food intake (meal) paradigms test to measure satiation(FIG. 6A). During a nutrient drink test, females with abnormal satiationconsumed 235 calories more prior to reach ‘usual’ fullness (p<0.001) and600 calories more prior to reach ‘maximal’ fullness (p<0.001); maleswith abnormal satiation consumed 514 calories more prior to reach‘usual’ fullness (p<0.001) and 752 calories more prior to reach‘maximal’ fullness (p<0.001) compared to individuals with obesity andnormal satiation. During the ad libitum buffet meal, females withabnormal satiation consumed 287 calories more prior to reach fullness(p<0.001) and males consumed 159 calories more prior to reach fullness(p=0.03) compared to individuals with obesity and normal satiation.Within obesity the sub-group with abnormal—or lack of—satiation consumedsignificant more calories in one meal, suggesting a deficiency in thestop signals and a hungry brain phenotype.

Abnormal Satiety Group

Accelerated gastric emptying was chosen as a surrogate for abnormalsatiety based on the main fact that is an objective, reproducible test,whiles other tests, such as visual analog scores are subjectivesensations of satiety. In female participants with obesity and abnormalsatiety, their gastric emptying (GE) was 40% GE solids T^(1/4)(p<0.001), 30% GE solids T^(1/2) (p<0.001) and 22% GE liquids T^(1/5)(p=0.01) faster compared to normal satiety. In male participants withobesity and abnormal satiety, their gastric emptying was 44% GE solidsT^(1/4) (p=0.005), 38% GE solids T^(1/2) (p<0.001) and 33% GE liquidsT^(1/2) (p=0.05) faster compared to normal satiety (FIG. 7A). Thegastric volume fasting and postprandial is smaller in participants withabnormal satiety compared to those with normal satiety when measured bySPECT (FIG. 7B). Additionally, individuals with abnormal satiety havelower levels of gastrointestinal satiety hormones, GLP-1 (p=0.005) andPYY3-36 (p=0.01) at 90 minutes after a meal. Individuals with abnormalsatiation have gastrointestinal satiety hormones similar to historicalcontrols with normal weight (see, e.g., Acosta et al., 2015Gastroenterology 148:537-546); and individuals in the ‘other’ group alsohave very low levels of these hormones, despite of having normal gastricemptying. However, the correlation of food intake when reach ‘usual’fullness in the nutrient drink test to the secretion of PYY3-36 islinear (r=0.42, p<0.001) and significant in individuals with normalsatiety and this correlation disappear in individuals with normalsatiety, suggesting an inadequate response of the PYY3-36 secretingenteroendocrine (EE) cells to the meal challenge. Enteroendocrine (EE)cells are real-time nutrient, bile and microbiota sensors that regulatefood intake, brain-gut communication, gastrointestinal motility, andglucose metabolism. EE cell function can be studied indirectly bymeasuring plasma levels of hormones such as GLP-1 or PYY, and lessfrequently EE cells are studied as part of whole intestinal tissue.These results suggested a hungry gut phenotype.

Hedonic Group

There is a sub-group within participants with obesity which have a verystrong psychological component that may predispose them to obesity,labeled here as a ‘hedonic’ sub-group. Likely this group is acquiringmost of their calories from emotional eating, cravings andreward-seeking behaviors while having appropriate sensations ofsatiation and satiety. Individuals in the hedonic group have higherlevels of anxiety (p<0.001) and depression (p<0.001); and lower levelsof self-esteem (p=0.002) when compared to other individuals withobesity. The hedonic group has a lower level of serum fasting tryptophancompared to the other groups (p=0.004, FIG. 8 ). Tryptophan is aprecursor of serotonin and melatonin, which has been associated withdepression, cravings and obesity.

Slow Metabolism Group

A sub-cohort of our population who completed indirect calorimetrytesting was studied and individuals in the slow metabolism group havesignificant lower resting energy expenditure (90% of predicted) comparedto the other groups of obesity (100% of predicted, p=0.032) wereidentified (FIG. 9A). The slow metabolism group have significant lowermeasured resting energy expenditure (kcal/day) that other groups(p<0.05) (FIG. 9B). Individuals with slow metabolism have lower systolicblood pressure (p=0.019), higher heart rate (p=0.05) higher self-steem(p=0.004). When body composition was measured using a dexa scan, therewas not difference in calculated BMI or measured total fat mass amongthe obesity groups, however, individuals with slow metabolism had lowermuscle (lean) mass compared to the other obesity groups (ANOVA p<0.05),FIG. 9C). Metabolites in patients with slow metabolism compared tonormal metabolism (other or rest) were significant different (p<0.05):higher than other groups: alanine, isocaproic acid, phosphoetahnolamine,phenylalanine, tyrosine, alpha-amino-N-butyric acid, sarcasine, andlower than other groups: 1-methylhistidine (FIG. 9D).

Obesity Phenotypes Biomarker

The applicability of obesity-related phenotypes as actionable biomarkerswas tested in three pilot, proof of concept studies (see, e.g., Acostaet al., 2015 Gastroenterology 148:537-546; Acosta et al., 2015 PhysiolRep 3; and Halawi et al., 2017 Lancet Gastroenterol Hepatol 2:890-899).First, in a single-center, randomized, parallel-group, double-blind,placebo-controlled, 14-day study, the effects ofPhentermine-topiramate-ER (PhenTop) (7.5/46 mg, orally, daily) ongastric emptying (GE) and volume, satiation, satiety, and fasting andpostprandial GI hormones was evaluated in 24 obese adults. Patients withan abnormal baseline satiation test had greater mean weight loss toPhenTop ER compared to those with normal satiation (p=0.03). In a secondplacebo-controlled trial, the effect of exenatide was studied, 5 μg SQ,twice daily for 30 days, on GE, satiety, satiation and weight loss in 20obese participants with obesity and abnormal satiety. The average weightloss was 1.3 kg for exenatide and 0.5 kg for the placebo group (p=0.06),suggesting that patients with abnormal satiety may be good candidatesfor weight loss with a GLP-1 receptor agonist). Subsequently, in aprospective, NIH-funded, randomized, placebo controlled clinical trialto study the effects of liraglutide 3 mg, SQ, over 16 weeks on obesityphenotypes and weight in 40 obese patients. Compared to placebo,liraglutide delayed GE of solids at 5 (p<0.0001) and 16 (p=0.025) weeks,caused significant weight loss and increased satiation. At 5 and 16weeks, GE T_(1/2) correlated with change in weight loss on liraglutide(all p<0.02). These results demonstrate that obesity-related phenotypescan predict response to obesity pharmacotherapy.

Phentermine-Topiramate and Obesity Phenotypes

The effects of phentermine-topiramate-ER (PhenTop) (7.5/46 mg, orally,daily) was evaluated on GE, GV, satiation, satiety, and fasting andpostprandial gut hormones as described elsewhere (see, e.g., Acosta etal., 2015 Gastroenterology 148:537-546). PhenTop was associated withreduced food intake at buffet meal (mean Δ 260 kcal, p=0.032) anddelayed GE solids (mean Δ GE4h 6%, p=0.03; and Δ GE T½ 19 min, p=0.057).There were no significant differences in GV, satiation, GE of liquidsand GI hormones. Patients on PhenTop had greater mean weight loss of 1.4kg than placebo (p=0.03). Weight loss on PhenTop was significantlyassociated with kcal intake at a prior satiety test. These resultsdemonstrate that PhenTop reduces food intake and delays GE of solids,indicating that patients having an obesity phenotype of Group 1 (lowsatiation), are likely responsive to treatment with PhenTop.

Exenatide and Obesity Phenotypes

The effects of exenatide (5 μg, SQ, twice daily for 30 days) wasevaluated on GE, satiety, and weight loss as described elsewhere (see,e.g., Acosta et al., 2015 Physiological Rep. 3(11)). Exenatide, aglucagon-like peptide-1 (GLP-1) agonist, had a very significant effecton GE of solids (p<0.001) and reduced calorie intake at a buffet meal byan average 130 kcal compared to placebo. The average weight loss was 1.3kg for exenatide and 0.5 kg for the placebo group (FIG. 11 ). Theseresults demonstrate that exenatide reduces food intake and delays GE ofsolids, indicating that a prior accelerated gastric emptying testpredicts weight loss with exenatide; see, also, Acosta et al., 2015Physiological Rep. 3(11)).

Surgery and Obesity Phenotypes

The best responders to the intragastric balloon therapy were identifiedas described elsewhere (see, e.g., Abu Dayyeh et al., “Baseline GastricEmptying and its Change in Response to Diverse Endoscopic BariatricTherapiesGastric Emptying Predict Weight Change Response to EndoscopicBariatric Therapies in a Large Cohort,” IFSO annual meeting, 2015) asindividuals with an accelerated gastric emptying (p<0.001) and thegreater delay in gastric emptying after intragastric balloon placement(p<0.001).

Liraglutide and Obesity Phenotypes

A prospective, randomized clinical trial with liraglutide, a long-actingGLP-1 receptor agonist, was completed. The effects of liraglutide andplacebo were compared over 16 weeks on gastric motor functions,satiation, satiety and weight in obese patients. The study was arandomized, double-blind, placebo-controlled trial of subcutaneousliraglutide, 3 mg, with standardized nutritional and behavioralcounseling. Forty adult, otherwise healthy local residents with BMI≥30kg/m² were randomized. Liraglutide or placebo was escalated by 0.6mg/day each week for 5 weeks and continued until week 16. At baselineand after 16 weeks' treatment, weight, gastric emptying of solids (GES,primary endpoint), large fasting gastric volumes, satiation, and satietywere measured. GES was also measured at 5 weeks. Statistical analysiscompared treatment effects using ANCOVA (with baseline measurement ascovariate). Effect of liraglutide on GES T^(1/2) at 5 and 16 weeks inthe liraglutide group was analyzed by paired t-test. Seventeenparticipants were analyzed in the liraglutide group (n=19 randomized)and 18 in the placebo group (n=21 randomized).

Compared to placebo, liraglutide retarded GES at 5 (p<0.0001) and 16(p=0.025) weeks, caused significant weight loss and increased satiation.In 16 weeks, the total body weight loss for the liraglutide group was6.1±2.8 kg (SD) compared to 2.2±5 kg control group (p=0.0096). There wastachyphylaxis to GES effects of liraglutide from 5 to 16 weeks'treatment. At 5 and 16 weeks, GES T^(1/2) correlated with A weight losson liraglutide (all p<0.02). Nausea was the most common adverse event inthe liraglutide group (63.2%) compared to placebo (9.5%). Liraglutide,3.0 mg, significantly delays GES after 5 and 16 weeks' treatment;effects on weight loss are associated with absolute value of GES T^(1/2)on liraglutide.

These results demonstrate that liraglutide significant weight loss andincreased satiation, indicating that a prior low satiety test predictsweight loss with liraglutide.

Individualized Therapy

The identification of obesity-related phenotypes based on an‘actionable’ classification and potential applicability for managementof obesity could have a significant impact on the obesity epidemic.

FIG. 12 shows exemplary individualized obesity interventions based uponobesity phenotypes.

The algorithm described in Example 1 was applied to 29 new patients withobesity (Table 9). Data from (intervention) pharmacotherapy and controlswere acquired retrospectively. Groups were matched for age, gender andBMI. Results were compared the outcome to 66 patients previously treatedby obesity experts.

TABLE 9 Obesity Patient Characteristics. Historical P Demographics CasesControls value N 55 175  Age   46 ± 1.8 50 ± 1 0.03 Gender (F) 67% 73%Race (W) 93% 89% Weight (kg) 115 ± 3  116 ± 1.8 BMI 39.8 ± 1  41.6 ± 0.6Co-morbidities % 47/41/45/49/36 43/33/35/36/30 (DM/HTN/DJD/OSA/HLD)*MEDS % 24 22 Phentermine 25 23 Phen-Top ER  6 10 Lorcaserin 19 25Liraglutide 3 mg 12  4 Bupropion-Naltrexone SR  4  6 Other Ns: notstatistical significant difference

The algorithm predicted the obesity group and interventionresponsiveness of the new participants with over 90% sensitivity andspecificity (FIG. 13 , FIG. 14 , and Table 10). The controls were seenin the weight management clinic by a physician expert of obesity andoffered standard of care for obesity management and pharmacotherapy. Thecurrent standard of care suggests that pharmacotherapy needs to beselected based on patient—physician preference, mainly driven by sideeffects and other comorbidities. The cases were seen in the weightmanagement clinic by a physician expert of obesity and offeredobesity-phenotype guided pharmacotherapy for obesity management. Thephenotypes seen were abnormal satiation (25%), abnormal satiety (20%),abnormal behavior (20%) and other (35%).

TABLE 10 Total body weight in response to individualized intervention.Total Body Weight Loss, % Controls Cases P value 3 months (# patients)2.7 ± 0.5 (66)  6.1 ± 0.8 (29) 0.0008 6 months (# patients) 4.7 ± 0.7(57) 10.7 ± 1.2 (22) 0.0001 9-12 months (# patients) 5.7 ± 1.2 (36) 12.9± 1.9 (15) 0.0025

The intervention group had 74% responders (defined as those who lossmore than 3% in the first month) compared to 33% in the control group.The control group number of responders was similar to the published inthe current obesity literature. The significant improvement ofresponders resulted in a total body weight loss of 12.9 kg in theintervention group compared to 5.8 kg in the control group at 9 months.

The algorithm was also applied to 12 patients with obesity, who sawtheir weight loss plateau during the treatment for obesity with anintragastric balloon. These individuals saw weight loss plateau duringmonth 3 and 6 of treatment with the balloon. At month 6, the algorithmwas applied to the intervention group compared to the controls.

Summary

It was found that obesity can be sub-grouped in: abnormal satiation(16%), abnormal satiety (16%), hedonic (19%), slow metabolism (32%) andmixed group (17%). Deeper characterization within each subgroupidentifies specific disturbances of function. Thus, in the group withabnormal satiation measured by two different feeding paradigms (adlibitum buffet meal and nutrient drink test), compared to lean controlsand other groups of obesity; this is summarized as “hungry brainphenotype”. In the group with abnormal satiety, there is a suboptimalresponse of the gut to food intake, manifested as accelerated gastricemptying and decrease in peak postprandial levels of satiety hormonessuggesting a “hungry gut phenotype”. In the hedonic group, there areincreased levels of anxiety, depression, and cravings with low levels ofserum tryptophan compared to the other groups. The slow metabolism grouphas decreased resting energy expenditure compared to other groups. Sinceidentifying the obesity subgroups by deep phenotyping is limited to fewacademic centers, a fasting blood multi-omic test was developed andvalidated that predict the obesity subgroups (ROC >90% AUC). This bloodtest provides segmentation of diverse sub-phenotypes of obesity, has thepotential to select patients for individualized treatment from the seaof obesity heterogeneity, facilitates our understanding of humanobesity, and may lead to future treatment based on actionablebiomarkers.

These results demonstrate that obesity phenotype groups can be used topredict treatment response, and can be used to guide individualizedtreatment strategies (e.g., pharmacotherapy and/or bariatric endoscopy).The obesity phenotype guided intervention doubled the weight loss inpatients with obesity.

Example 3: Obesity Phenotypes and Patient Sub-Populations

To validate further the applicability of the obesity phenotypes, thefact that each sub-group may have unique abnormalities compared to theother groups when tested with previously validated or reported findingsin common obesity was interrogated.

The model described above was run independently for female and malesub-populations of patients. Characteristics of a complete population isas denoted in Table 11.

TABLE 11 Whole Cohort (181 patients). Mean + #pts w/ #pts w/ Trait SD >2SD # pts Median >90% trait % pts >75% trait % pts HADS-A 3.4 + 2.5 9 173 7 29 16% 6 46 25% HADS-D 1.54 + 1.74 5 20 1 4 25 14% 3 24 13% VTF706 + 296 1337 9 630 1080 21 12% 900 44 24% MTV 1286 + 417  2142 13 12721896 24 13% 1539 46 25% VAS - Full 70 + 14 −40 2 72 48 18 10% 61 44 24%SGE T½ 99.5 + 25.8 −47 5 98 70.8 17  9% 81 43 24% Buffett 917 + 295 160416 916 1357 23 13% 1184 44 24%

Unique analytes were identified when this cohort was separated intofemale and male sub-populations as shown in Table 12 and Table 13.

TABLE 12 Female sub-population (134 patients). Mean + #pts w/ #pts w/Trait SD >2 SD # pts Median >90% trait % pts >75% trait % pts HADS-A 4 93 7 20 15% 6 34 25% HADS-D 2 5 1 4 13 10% 2 30 22% VTF 632 1123 600 99014 10% 750 40 30% MTV 1174 1879 1185 1618 13 10% 1422 35 26% VAS - Full70 39 72 49 14 10% 61 34 25% SGE T½ 105 56 102 77 14 10% 90 32 24%Buffett 896 1394 848 1279 13 10% 1028 34 25%

TABLE 13 Male sub-population (47 patients). Mean + #pts w/ #pts w/ TraitSD >2 SD # pts Median >90% trait % pts >75% trait % pts HADS-A 4 9 4 7 817%  6 12 26% HADS-D 2 7 2 5 6 13%  3 17 36% VTF 959 1659 900 1524 4 9%1125 11 23% MTV 1626 2517 1659 2180 4 9% 1951 11 23% VAS - Full 69 41 7247 4 9% 63 11 23% SGE T½ 83 34 78 56 6 13%  68 11 23% Buffett 1248 18931222 1693 4 9% 1469 11 23%

When analytes were identified in female and male sub-populations, theconcentration of metabolites differed. See the concentrations as shownin Table 14.

TABLE 14 Concentrations of analytes in targeted metabolites. highestlower limit of Coefficient standard quantification of variation analyteconcentration (LOQ) (CV) Histidine 930 0.155 1.2% Hydroxyproline 9300.155 1.5% 1-Methylhistidine 930 0.155 0.5% 3-Methylhistidine 930 0.1550.8% Asparagine 1000 0.167 0.7% Phosphoethanolamine 1000 0.167 0.4%Arginine 930 0.155 0.5% Carnosine 930 0.155 2.0% Taurine 930 0.155 1.5%Anserine 930 0.155 2.2% Serine 930 0.155 0.7% Glutamine 4000 0.667 0.2%Ethanolamine 930 0.155 2.0% Glycine 930 0.155 1.6% Aspartic Acid 9300.155 0.7% Sarcosine 930 0.155 0.4% Citrulline 930 0.155 1.6% GlutamicAcid 930 0.155 0.6% beta-Alanine 930 0.155 0.5% Threonine 930 0.155 1.0%Alanine 930 0.155 0.5% gamma-Amino- 930 0.155 1.3% N-butyric-acidalpha-Aminoadipic- 930 0.155 0.0% acid beta-Aminoisobutyric- 930 0.1550.4% acid Proline 930 0.155 1.0% Hydroxylysine 1 930 0.155 0.6%Hydroxylysine 2 930 0.155 0.3% alpha-Amino- 930 0.155 0.0%N-butyric-acid Ornithine 930 0.155 0.9% Cystathionine 1 930 0.155 1.0%Cystathionine 2 930 0.155 0.6% Lysine 930 0.155 0.2% Cystine 930 0.1551.7% Tyrosine 930 0.155 0.4% Methionine 930 0.155 1.2% Valine 930 0.1551.7% Isoleucine 930 0.155 2.3% allo-Isoleucine 930 0.155 0.9%Homocystine 930 0.155 1.0% Leucine 930 0.155 0.5% Phenylalanine 9300.155 1.6% Tryptophan 930 0.155 1.1% Acetylcholine 1600 0.10 5.6%Adenosine 1600 0.10 0.5% Norepinephrine 1600 0.10 1.0% Dopamine 16000.10 0.7% Serotonin 1600 0.10 1.0% acetic acid 6651 5.5  25% propionicacid 4955 1.03  7% isobutyric acid 4792 1.00  2% butyric acid 5379 1.12 4% isovaleric acid 4030 0.84  15% valeric acid 5393 1.12  5% isocaproicacid 3532 0.74  12% caproic acid 2901 0.60  8%

Unique targets identified in a sub-population can serve to find a uniquetreatment: for example TCF7L/2 genetic variant can be used to identify agroup with abnormal satiety; or a simple test such as gastric emptyingcan be used to clef ne abnormal satiety.

The model described above is also run independently for additionalsub-populations of patients. For example, the model can be run onpatients of specific ages (e.g., youth such as people from birth toabout 18, adults such as people 18 or older), and specific life stages(e.g., perimenopausal women, menopausal women, post-menopausal women,and andropausal men).

Sub-populations of patients demonstrated analyte differences betweenobesity groups that were not seen in a full population of patients.

Example 4: Selecting Treatment(s) for Obesity Therapy

When an individual is treated with any weight loss intervention, his/herphenotype can assist in selecting a treatment.

Study Design

In a 12 week, randomized, double-blinded, active controlled trial, with9 month open-label extension of 200 participants with obesity; theweight loss response rate to obesity-phenotype-guided pharmacotherapy(intervention) vs. non-phenotype guided (randomly selected)pharmacotherapy (control) in patients with obesity is compared. All 200participants are phenotyped and the medication selection is randomly anddouble blinded (to physician, study team, and participant) to theFDA-approved medicine suggested by the phenotype or to anotherFDA-approved medicine not suggested by the phenotype.

All participants receive a standard intense lifestyle intervention,which consists of 2 visits with registered dietitian. The phenotypicstudies include (all performed in same day in the following order):fasting blood collection, resting energy expenditure, gastric emptyingwith meal for breakfast, behavioral questionnaires, and buffet meal testfor lunch. Blood is collected for assessment of metabolomic biomarkers,gastrointestinal hormones, DNA (blood and buccal swab), andpharmacogenomics. Stool samples are collected for microbiome and bileacid. Participants return to the CRTU to pick up medication based on therandomization and discuss the pharmacogenomics results. All participantsare contacted at 4 and seen at 12 weeks (current standard in practice).A stool sample and a fasting blood sample are collected at the 12-weekvisit. At the 12-week visit, participants will be unblinded to their“obesity-related phenotype” and they could contact their physician tocontinue a FDA-approved medication as part of clinical care. Study teamwill prospectively follow the patients' weight, waist circumference anduse of obesity medications every 3 months for 1 year.

Randomization and Allocation

A computer generated randomization is based on guiding pharmacotherapybased on the phenotype or randomly as current standard of care.Allocations are concealed.

Participants

A study cohort includes 200 patients with obesity (BMI>30 kg/m²).Participants that agree to pharmacotherapy treatment are invited toparticipate in the phenotypic assessment of their obesity that willguide (or not) the pharmacotherapy.

Inclusion Criteria

-   -   a) Adults with obesity (BMI≥30 Kg/m²); these are otherwise        healthy individuals with no unstable psychiatric disease and        controlled comorbidities or other diseases.    -   b) Age: 18-75 years.    -   c) Gender: Men or women. Women of childbearing potential have        negative pregnancy tests within 48 hours of enrolment and before        each radiation exposure.

Exclusion Criteria

-   -   a) Abdominal bariatric surgery    -   b) Positive history of chronic gastrointestinal diseases, or        systemic disease that could affect gastrointestinal motility, or        use of medications that may alter gastrointestinal motility,        appetite or absorption, e.g., orlistat, within the last 6        months.    -   c) Significant untreated psychiatric dysfunction based upon        screening with the Hospital Anxiety and Depression Inventory        (HAD), and the Questionnaire on Eating and Weight Patterns        (binge eating disorders and bulimia). If such a dysfunction is        identified by an anxiety or depression score >11 or difficulties        with substance or eating disorders, the participant will be        excluded and given a referral letter to his/her primary care        doctor for further appraisal and follow-up.    -   d) Hypersensitivity to any of the study medications.    -   e) No contraindications to all FDA-approved medications

Anthropometrics and Phenotype Studies

Anthropometrics Measurements: are taken of hip-waist ratio, height,weight, blood pressure, pulse at baseline, randomization day and week12.

Phenotype studies at baseline: After an 8-hour fasting period, and thefollowing validated quantitative traits (phenotypes) are measured atbaseline:

-   -   a) The DEXA scan (dual energy x-ray absorptiometry) measures        body composition.    -   b) Resting energy expenditure: is assessed by indirect        calorimetry with a ventilated hood.    -   c) Gastric emptying (GE) of solids by scintigraphy: The        primary-endpoint is gastric half-emptying time (GE t_(1/2)) as        described elsewhere (see, e.g., Acosta et al., 2015        Gastroenterology 148:537-546; Vazquez et al., 2006        Gastroenterology 131:1717-24; and Camilleri et al., 2012        Neurogastroenterology and Motility 24:1076).    -   d) Appetite (hunger level) by visual analog score fasting and        after standard meal for GE and prior to the Satiation test as        described elsewhere (see, e.g., Acosta et al., 2015        Gastroenterology 148:537-546).    -   e) Satiation is measured by ad-libitum buffet meal to measure        total caloric intake and macronutrient distribution in the        chosen food. Satiation is reported in calories consumed at        fullness (satiation) as described elsewhere (see, e.g., Acosta        et al., 2015 Gastroenterology 148:537-546).    -   f) Satiety by visual analog score postprandial after standard        meal for GE and after to the Ad-libitum meal test for every 30        minutes for 2 hours as described elsewhere (see, e.g., Acosta et        al., 2015 Gastroenterology 148:537-546). Satiety is measured in        length of time of fullness.    -   g) Self-administered questionnaires assessing affect, physical        activity levels, attitudes, body image, and eating behavior;        details of each questionnaire are provided below.    -   h) Sample collection, handling and storage: Samples are        collected after an overnight fast (of at least 8 hours) in the        morning. Plasma was preserved following standard guidelines and        protein degradation inhibitors, kalikrein and DPP-IV inhibitors        are added to preserve the samples. Samples are stored at −80° C.        -   a. Plasma gastrointestinal hormones (Total and active            Ghrelin, GLP-1, CCK. PYY and bile acids) by            radioimmunoassay, measured fasting, and 15, 45, and 90            minutes postprandial, with the primary endpoint being the            peak postprandial level (test should be done simultaneously            to GE).        -   b. Targeted Metabolomics: Targeted metabolomics of salient            classes of compounds in plasma samples are performed using            mass spectrometry. Amino acids plus amino metabolites are            quantified in plasma by derivatizing with            6-aminoquinolyl-N-hydroxysuccinimidyl carbamate according to            Waters MassTrak kit. A 10-point calibration standard curve            is used for quantification of unknowns using a triple-stage            quadrupole mass spectrometer (Thermo Scientific TSQ Quantum            Ultra) coupled with an ultra-performance liquid            chromatography (UPLC) system (Waters Acquity UPLC). Data            acquisition is performed using multiple-reaction monitoring            (MRM). Concentrations of 42 analytes in each sample are            calculated against their respective calibration curves with            a measurement precision of <5%. Essential nonesterified            fatty acid (NEFA) concentrations, such as myristic,            palmitic, palmetoleic palmitoelaidic, stearic, oleic,            elaidic, linoleic, linolenic and arachidonic, are measured            against a six-point standard curve by LC/MS/MS,            underivatized after extraction from plasma via negative            electrospray ionization (ESI) and multiple reaction            monitoring conditions. This technique was developed to            replace the GC/MS method where NEFAs required methylation            before analysis. This technique reduces the uncertainty as            to whether the methylation step increases FFA concentrations            by inadvertently hydrolyzing other lipid classes. Intra CV            is <3% for all analytes.        -   c. Blood DNA.        -   d. Buccal Swab DNA for OneOme pharmacogenomics testing.            -   i. Pharmacogenomics: Patients who have met the inclusion                and exclusion criteria provide a one-time buccal                scraping. 72 variants in 22 pharmacogenes, with seven                cytochrome P450 enzymes (CYP1A2, CYP2B6, CYP2C9,                CYP2C19, CYP2D6, CYP3A4, and CYP3A5) covering                approximately 90 percent of human drug oxidation and                nearly 50 percent of commonly used medications, and 15                genes related to drug action or metabolism (COMT, DPYD,                DRD2, F2, F5, GRIK4, HTR2A, HTR2C, IL28B, NUDT15, OPRM1,                SLCO1B1, TPMT, UGT1A1, and VKORC1) are assessed. Results                for the patient are placed into the patient EHR to be                utilized for clinical treatment decisions. Through chart                review, including the patient's current medication list                as stated in the EHR, previously reported medication                inefficacy and intolerance is documented. This data is                entered into a database.    -   i) Stool is collected and stored to study microbiome, short        chain fatty acids, and bile acids.        -   Studies at 12-week visit:    -   j) Stool and fasting blood sample are collected and stored.        Stool is used to measure microbiome, short chain fatty acids and        bile acids (as above). Fasting blood will be used to GI hormones        and metabolomics (as above).

Questionnaires to Assess GI Symptoms and Behavioral Disorders

Participants complete a series of questionnaires: Weight managementQuestionnaire (Mayo Clinic®), the and the Hospital Anxiety andDepression Inventory [HAD (see, e.g., Zigmond et al., 1983 ActaPsychiatrica Scandinavica 67:361-70)] to appraise the contribution ofaffective disorder.

Behavioural Questionnaires

-   -   a. AUDIT-C Alcoholism Screening Test—This score is used in        screening by the study physician/nurse coordinator.    -   b. Eating Disorders Questionnaire—The Questionnaire on Eating        and Weight Patterns-Revised, is a valid measure of screening for        eating disorders which has been used in several national        multi-site field trials. Respondents are classified as binge        eating disorder, purging bulimia nervosa, non-purging bulimia        nervosa, or anorexia nervosa.    -   c. Body Image Satisfaction—The Multidimensional Body-Self        Relations Questionnaire provides a standardized attitudinal        assessment of body image, normed from a national body-image        survey. Items are rated on a 5-point scale, ranging from        1=Definitely Disagree to 5=Definitely Agree. A sub-scale, the        Body Areas Satisfaction Scale, is used to measure feelings of        satisfaction with discrete aspects of physical appearance (e.g.,        face, weight, hair). Cronbach's a values range from 0.70 to        0.89.    -   d. Eating Behaviors—The Weight Efficacy Life-Style Questionnaire        [WEL] is a 20-item eating self-efficacy scale consisting of a        total score and five situational factors: negative emotions,        availability, social pressure, physical discomfort, and positive        activities. Subjects are asked to rate their confidence about        being able to successfully resist the urge to eat using a        10-point scale ranging from 0=not confident to 9=very confident.    -   e. Physical Activity Level—The four-item Physical Activity        Stages of Change Questionnaire will be utilized to assess the        physical activity level of participants.

Standard of Care:

All participants receive standard of care which consists of 1) Intenselifestyle intervention, behavioral evaluation and treatment, and amedication as part of the regular clinic management for obesity.

Intense Lifestyle Intervention and Behavioral Treatment

All the participants will meet the multidisciplinary team which consistsof an Obesity Expert physician a registered dietitian nutritionist asstandard of care in our clinical practice. These appointments will beschedule in the clinic and will not be covered by the current protocol.All participants are guided to 1) Nutrition: Reduce dietary intake belowthat required for energy balance by consuming 1200-1500 calories per dayfor women and 1500-1800 calories per day for men; 2) Physical Activity:reach the goal of 10,000 steps or more per day; 3) Exercise: reach thegoal of 150 minutes or more of cardiovascular exercise/week; 4) Limitconsumption of liquid calories (i.e. sodas, juices, alcohol, etc.).

Pharmacotherapy for Obesity

Pharmacotherapy for the treatment of obesity can be considered if apatient has a body mass index (BMI)≥30 kg/m² or BMI>27 kg/m² with acomorbidity such as hypertension, type 2 diabetes, dyslipidemia andobstructive sleep apnea. Medical therapy should be initiated with doseescalation based on efficacy and tolerability to the recommended dose.An assessment of efficacy and safety at 4 weeks is done. In both groups,medications are assessed for drug interactions and potential sideeffects as standard of care.

Medication selection: Once the phenotype tests are completed the resultsare filled in an algorithm to assist on the decision of the medicationselection as described elsewhere (see, e.g., Acosta et al., 2015Gastroenterology 148:537-546; Camilleri et al., 2016 Gastrointest.Endosc. 83:48-56; and Acosta et al., 2015 Physiological Rep. 3(11)). Anexample is below:

TEST Abnormal result Example 1 Example 2 Example 3 Example 4Satiation >1139 kcal 1400 kcal 1000 kcal 1100 kcal 1050 kcal (Ad libitumBuffet Meal) Satiety SGE T^(1/2) <85 102 min 80 min 105 min 110 min(Gastric emptying) min or GE 1 hr >35% Behavioral Traits HADS A&D >6points 5 4 9 3 (Questionnaires) Energy Expenditure <85% predicted 92%93% 95% 82% (Resting EE) Phenotype — Ab Satiation Ab Satiety Ab Psych AbE.E.

Once the decision is made on the “phenotype-guided” medication, pharmacywill assess whether patient is randomized to “intervention” or“control”. Based on the randomization, patient picks up the prescriptionfor 3 months. During the 3-month visit, participants are offered aprescription to continue the medication (if randomized to theintervention group) or to switch to the phenotype guided medication (ifrandomized to the control group). Patients who continue obesitypharmacotherapy are contact every three months for one year to monitortheir weight and comorbidities.

Control Group: Pharmacotherapy for Obesity

Standard of care pharmacotherapy for obesity recommends the followingdoses and regimen for weight loss:

-   -   Phentermine: 15-37.5 mg oral daily    -   Phentermine-Topiramate Extended Release (Qsymiak) at dose of        7.5/46 mg oral daily    -   Oral naltrexone extended-release/bupropion extended-release        (NBSR; Contrave®) at dose of 32/360 mg oral daily (divided in 2        tables in morning and 2 tablets in evening)    -   Liraglutide (Saxenda®) at dose of 3 mg subcutaneous daily

Intervention Group: By Obesity Phenotype Guided Pharmacotherapy

Participants in the intervention group will have 4 tests to assess 1)satiation, 2) satiety/return to hunger, 3) behavioral, or 4) energyexpenditure. As described in FIG. 12 pharmacotherapy will by guide basedon the phenotype. In case of a mixed pattern or multiple abnormalphenotypes, the most prominent phenotype is tackled.

Algorithm diagnostic:

-   -   1. satiation: Phentermine-Topiramate Extended Release (Qsymia®)        at dose of 7.5/46 mg oral daily    -   2. Satiety/return to hunger: Liraglutide 3 mg SQ daily    -   3. Behavioral/Psychological: Oral naltrexone        extended-release/bupropion extended-release (NBSR; Contrave®) at        dose of 32/360 mg oral daily (divided in 2 tables in morning and        2 tablets in evening); or    -   4. Energy expenditure: Phentermine 15 mg daily plus increase        physical activity.

Statistical Analyses

Primary endpoint: Total Body Weight Loss, kg (defined as weight changedfrom baseline to 12 weeks) in the obesity phenotype-guidedpharmacotherapy (intervention) vs. the randomly assigned pharmacotherapy(control) group.

The secondary end points will be percentage of responders (defined asnumber of participants who loss 5% or more of total body weight)compared to baseline in the obesity phenotype guided pharmacotherapy(intervention) group vs. standard of care at 4 and 12 weeks; percentageof responders with at least 10 and 15% at 12 weeks, and 10% at 6 monthsand 12 months; percentage of responders at 5%, 10% and 15%; percentageof responders within each obesity-phenotype group at 4 and 12 weeks; andside effects of medications. In the open-label extension, the total bodyweight loss is assessed at 24 and 52 weeks in both groups.

Statistical Analyses: A randomized, double-blinded, active controlledtrial of 200 participants with obesity to compare effects ofintervention compared to controls in weight loss. The analysis involvesan ANCOVA models, with the response being actual weight change; thecovariates to be considered include gender, and BMI (at baseline) atbaseline.

Sample size assessment and power calculation: The detectable effect sizein weight loss between groups of interest (intervention vs. control) isgiven in Table 15. Using a SD for the overall weight change (pre-post)of 2.8 kg, the differences between groups that could be detected withapproximately 80% power (2-sided a level of 0.05) for main effects areestimated. Thus, the sample size needed is 87 participants per group. Inorder to account for dropout, 100 participants per group are randomized.

TABLE 15 Mean difference (Δ) of total body Intervention Control weightloss in controls group (mean (# of (# of average 6.1 kg) vs.intervention group. participants) participants) Mean difference of 10%[6.7 vs. 6.1 kg) 343 343 Mean difference of 20% [7.3 vs. 6.1 kg) 87 87Mean difference of 30% [7.9 vs. 6.1 kg) 39 39

As each 50 patients complete the 12-week treatment phase, an interimanalysis is conducted by the study statistician for the purpose ofensuring (based on the observed coefficient of variation in the primaryresponses such as the proportion of weight difference of 20%) that thestudy still has sufficient power based on the sample size proposed inthe study.

OTHER EMBODIMENTS

It is to be understood that while the invention has been described inconjunction with the detailed description thereof, the foregoingdescription is intended to illustrate and not limit the scope of theinvention, which is defined by the scope of the appended claims. Otheraspects, advantages, and modifications are within the scope of thefollowing claims.

What is claimed is:
 1. A method for treating obesity in a mammal,wherein said method comprises: (a) identifying said mammal as having anintervention responsive obesity analyte signature in a sample obtainedfrom said mammal; and (b) administering an intervention to said mammal.2. The method of claim 1, wherein said sample is selected from the groupconsisting of a blood sample, a saliva sample, a urine sample, a breathsample, and a stool sample.
 3. The method of claim 2, wherein saidsample is a breath sample.
 4. The method of claim 2, wherein said sampleis a stool sample.
 5. A method for treating obesity in a mammal, whereinsaid method comprises administering an intervention to a mammalidentified as having an intervention responsive obesity analytesignature.
 6. The method of any one of claim 1 to claim 5, wherein saidmammal is a human.
 7. The method of any one of claim 1 to claim 6,wherein said intervention is effective to reduce the total body weightof said mammal by at least 4%.
 8. The method of any one of claim 1 toclaim 6, wherein said intervention is effective to reduce the total bodyweight of said mammal by from about 3 kg to about 100 kg.
 9. The methodof any one of claim 1 to claim 6, wherein said intervention is effectiveto reduce the waist circumference of said mammal by from about 1 inchesto about 10 inches.
 10. The method of any one of claim 1 to claim 9,wherein said obesity analyte signature comprises 1-methylhistine,serotonin, glutamine, gamma-amino-n-butyric-acid, isocaproic,allo-isoleucine, hydroxyproline, beta-aminoisobutyric-acid, alanine,hexanoic, tyrosine, phenylalanine, ghrelin, and peptide tyrosinetyrosine (PYY).
 11. The method of any one of claim 1 to claim 10,wherein said identifying further comprises obtaining results from aHospital Anxiety and Depression Scale (HADS) questionnaire.
 12. Themethod of claim 11, wherein said obesity analyte signature comprises apresence of serotonin, glutamine, isocaproic, allo-isoleucine,hydroxyproline, beta-aminoisobutyric-acid, alanine, hexanoic, tyrosine,and PYY, and an absence of 1-methylhistine, gamma-amino-n-butyric-acid,phenylalanine, ghrelin; wherein said HADS questionnaire result does notindicate an anxiety subscale; and wherein said mammal is responsive tointervention with phentermine-topiramate pharmacotherapy and/orlorcaserin pharmacotherapy.
 13. The method of claim 11, wherein saidobesity analyte signature comprises a presence of 1-methylhistine,allo-isoleucine, hydroxyproline, beta-aminoisobutyric-acid, alanine, andphenylalanine, and an absence of serotonin, glutamine,gamma-amino-n-butyric-acid, isocaproic, hexanoic, tyrosine, ghrelin, andPYY; wherein said HADS questionnaire result not indicate an anxietysubscale; and wherein said mammal is responsive to intervention withliraglutide pharmacotherapy.
 14. The method of claim 11, wherein saidobesity analyte signature comprises a presence of serotonin, and anabsence of 1-methylhistine, glutamine, gamma-amino-n-butyric-acid,isocaproic, allo-isoleucine, hydroxyproline, beta-aminoisobutyric-acid,alanine, hexanoic, tyrosine, phenylalanine, ghrelin, and PYY; whereinsaid HADS questionnaire result indicates an anxiety subscale; andwherein said mammal is responsive to intervention withnaltrexone-bupropion pharmacotherapy.
 15. The method of claim 11,wherein said obesity analyte signature comprises a presence of1-methylhistine, glutamine, gamma-amino-n-butyric-acid, isocaproic,allo-isoleucine, beta-aminoisobutyric-acid, alanine, hexanoic, tyrosine,phenylalanine, PYY, and an absence of serotonin, hydroxyproline, andghrelin; wherein said HADS questionnaire result indicates an anxietysubscale; and wherein said mammal is responsive to intervention withnaltrexone-bupropion pharmacotherapy.
 16. The method of claim 11,wherein said obesity analyte signature comprises a presence of1-methylhistine, serotonin, glutamine, gamma-amino-n-butyric-acid,isocaproic, allo-isoleucine, alanine, tyrosine, ghrelin, PYY, and anabsence of hydroxyproline, beta-aminoisobutyric-acid, hexanoic, andphenylalanine; wherein said HADS questionnaire result indicates ananxiety subscale; and wherein said mammal is responsive to interventionwith phentermine pharmacotherapy.
 17. A method for identifying an obesemammal as being responsive to treatment with an intervention, whereinsaid method comprises: (a) determining an obesity analyte signature in asample obtained from said mammal, wherein said obesity analyte signaturecomprises 1-methylhistine, serotonin, glutamine,gamma-amino-n-butyric-acid, isocaproic, allo-isoleucine, hydroxyproline,beta-aminoisobutyric-acid, alanine, hexanoic, tyrosine, phenylalanine,ghrelin, and peptide tyrosine tyrosine (PYY); and (b) classifying saidmammal as having an intervention responsive obesity analyte signaturebased upon the presence and absence of analytes in said obesity analytesignature.
 18. The method of claim 17, wherein said mammal is a human.19. The method of any one of claim 17 to claim 18, wherein said sampleis selected from the group consisting of a blood sample, a salivasample, a urine sample, a breath sample, and a stool sample.
 20. Themethod of claim 19, wherein said sample is a breath sample.
 21. Themethod of claim 19, wherein said sample is a stool sample.
 22. Themethod of any one of claim 17 to claim 21, said method furthercomprising obtaining results from a Hospital Anxiety and DepressionScale (HADS) questionnaire.
 23. The method of claim 22, wherein saidobesity analyte signature comprises a presence of serotonin, glutamine,isocaproic, allo-isoleucine, hydroxyproline, beta-aminoisobutyric-acid,alanine, hexanoic, tyrosine, and PYY, and an absence of 1-methylhistine,gamma-amino-n-butyric-acid, phenylalanine, ghrelin; wherein said HADSquestionnaire result does not indicate an anxiety subscale; and whereinsaid mammal is classified as being responsive to intervention withphentermine-topiramate pharmacotherapy and/or lorcaserinpharmacotherapy.
 24. The method of claim 22, wherein said obesityanalyte signature comprises a presence of 1-methylhistine,allo-isoleucine, hydroxyproline, beta-aminoisobutyric-acid, alanine, andphenylalanine, and an absence of serotonin, glutamine,gamma-amino-n-butyric-acid, isocaproic, hexanoic, tyrosine, ghrelin, andPYY; wherein said HADS questionnaire result not indicate an anxietysubscale; and wherein said mammal is classified as being responsive tointervention with liraglutide pharmacotherapy.
 25. The method of claim22, wherein said obesity analyte signature comprises a presence ofserotonin, and an absence of 1-methylhistine, glutamine,gamma-amino-n-butyric-acid, isocaproic, allo-isoleucine, hydroxyproline,beta-aminoisobutyric-acid, alanine, hexanoic, tyrosine, phenylalanine,ghrelin, and PYY; wherein said HADS questionnaire result indicates ananxiety subscale; and wherein said mammal is classified as beingresponsive to intervention with naltrexone-bupropion pharmacotherapy.26. The method of claim 22, wherein said obesity analyte signaturecomprises a presence of 1-methylhistine, glutamine;gamma-amino-n-butyric-acid, isocaproic, allo-isoleucine,beta-aminoisobutyric-acid, alanine, hexanoic, tyrosine, phenylalanine,PYY, and an absence of serotonin, hydroxyproline, and ghrelin; whereinsaid HADS questionnaire result indicates an anxiety subscale; andwherein said mammal is classified as being responsive to interventionwith naltrexone-bupropion pharmacotherapy.
 27. The method of claim 22,wherein said obesity analyte signature comprises a presence of1-methylhistine, serotonin, glutamine, gamma-amino-n-butyric-acid,isocaproic, allo-isoleucine, alanine, tyrosine, ghrelin, PYY, and anabsence of hydroxyproline, beta-aminoisobutyric-acid, hexanoic, andphenylalanine; wherein said HADS questionnaire result indicates ananxiety subscale; and wherein said mammal is classified as beingresponsive to intervention with phentermine pharmacotherapy.
 28. Themethod of any one of claim 1 to claim 9, wherein said obesity analytesignature comprises HTR2C, GNB3, FTO, iso-caproic acid,beta-aminoisobutyricacid, butyric, allo-isoleucine, tryptophan, andglutamine.
 29. The method of claim 28, wherein said identifying furthercomprises obtaining results from a Hospital Anxiety and Depression Scale(HADS) questionnaire.
 30. The method of claim 29, wherein said obesityanalyte signature comprises a presence of a single nucleotidepolymorphism (SNP) in HTR2C, POMC, NPY, AGRP, MC4R, GNB3, SERT, and/orBDNF; wherein said HADS questionnaire result does not indicate ananxiety subscale; and wherein said mammal is responsive to interventionwith phentermine-topiramate pharmacotherapy and/or lorcaserinpharmacotherapy.
 31. The method of claim 30, wherein said SNP isrs1414334.
 32. The method of claim 29, wherein said obesity analytesignature comprises a presence of a SNP in PYY, GLP-1, MC4R, GPBAR1,TCF7L2, ADRA2A,PCSK, and/or TMEM18; wherein said HADS questionnaireresult not indicate an anxiety subscale; and wherein said mammal isresponsive to intervention with liraglutide pharmacotherapy.
 33. Themethod of claim 32, wherein said SNP is rs7903146.
 34. The method ofclaim 29, wherein said obesity analyte signature comprises a presence ofa SNP in SLC6A4/SERT, and/or DRD2; wherein said HADS questionnaireresult indicates an anxiety subscale; and wherein said mammal isresponsive to intervention with naltrexone-bupropion pharmacotherapy.35. The method of claim 34, wherein said SNP is rs4795541.
 36. Themethod of claim 29, wherein said obesity analyte signature comprises apresence of a SNP in TCF7L2, UCP3, and/or ADRA2A; wherein said HADSquestionnaire result indicates an anxiety subscale; and wherein saidmammal is responsive to intervention with naltrexone-bupropionpharmacotherapy.
 37. The method of claim 36, wherein said SNP isrs1626521.
 38. The method of claim 29, wherein said obesity analytesignature comprises a presence of a SNP in FTO, LEP, LEPR, UCP1, UCP2,UCP3, ADRA2, KLF14, NPC1, LYPLAL1, ADRB2, ADRB3, and/or BBS1; whereinsaid HADS questionnaire result indicates an anxiety subscale; andwherein said mammal is responsive to intervention with phenterminepharmacotherapy.
 39. The method of claim 38, wherein said SNP isrs2075577.
 40. A method for identifying an obese mammal as beingresponsive to treatment with an intervention, wherein said methodcomprises: (a) determining an obesity analyte signature in a sampleobtained from said mammal, wherein said obesity analyte signaturecomprises HTR2C, GNB3, FTO, iso-caproic acid, beta-aminoisobutyricacid,butyric, allo-isoleucine, tryptophan, and glutamine; (b) obtainingresults from a Hospital Anxiety and Depression Scale (HADS)questionnaire; and (c) classifying said mammal as having an interventionresponsive obesity analyte signature based upon the presence and absenceof analytes in said obesity analyte signature.
 41. The method of claim40, wherein said mammal is a human.
 42. The method of any one of claim40 to claim 41, wherein said sample is selected from the groupconsisting of a blood sample, a saliva sample, a urine sample, a breathsample, and a stool sample.
 43. The method of claim 42, wherein saidsample is a breath sample.
 44. The method of claim 42, wherein saidsample is a stool sample.
 45. The method of any one of claim 40 to claim44, wherein said obesity analyte signature comprises a presence of asingle nucleotide polymorphism (SNP) in HTR2C, POMC, NPY, AGRP, MC4R,GNB3, SERT, and/or BDNF: wherein said HADS questionnaire result does notindicate an anxiety subscale; and wherein said mammal is classified asbeing responsive to intervention with phentermine-topiramatepharmacotherapy and/or lorcaserin pharmacotherapy.
 46. The method ofclaim 45, wherein said SNP is rs1414334.
 47. The method of any one ofclaim 40 to claim 44, wherein said obesity analyte signature comprises apresence of a SNP in PYY, GLP-1, MC4R, GPBAR1, TCF7L2, ADRA2A,PCSK,and/or TMEM18; wherein said HADS questionnaire result not indicate ananxiety subscale; and wherein said mammal is classified as beingresponsive to intervention with liraglutide pharmacotherapy.
 48. Themethod of claim 47, wherein said SNP is rs7903146.
 49. The method of anyone of claim 40 to claim 44, wherein said obesity analyte signaturecomprises a presence of a SNP in SLC6A4/SERT, and/or DRD2; wherein saidHADS questionnaire result indicates an anxiety subscale; and whereinsaid mammal is classified as being responsive to intervention withnaltrexone-bupropion pharmacotherapy.
 50. The method of claim 49,wherein said SNP is rs4795541.
 51. The method of any one of claim 40 toclaim 44, wherein said obesity analyte signature comprises a presence ofa SNP in TCF7L2, UCP3, and/or ADRA2A; wherein said HADS questionnaireresult indicates an anxiety subscale; and wherein said mammal isclassified as being responsive to intervention with naltrexone-bupropionpharmacotherapy.
 52. The method of claim 51, wherein said SNP isrs1626521.
 53. The method of any one of claim 40 to claim 44, whereinsaid obesity analyte signature comprises a presence of a SNP in FTO,LEP, LEPR, UCP1, UCP2, UCP3, ADRA2, KLF14, NPC1, LYPLAL1, ADRB2, ADRB3,and/or BBS1; wherein said HADS questionnaire result indicates an anxietysubscale; and wherein said mammal is classified as being responsive tointervention with phentermine pharmacotherapy.
 54. The method of claim53, wherein said SNP is rs2075577.